![1.0
Chapter 1
Dreams and Dreamers
The quest for artificial intelligence (AI) begins with dreams – as all quests do.
People have long imagined machines with human abilities – automata that
move and devices that reason. Human-like machines are described in many
stories and are pictured in sculptures, paintings, and drawings.
You may be familiar with many of these, but let me mention a few. The
Iliad of Homer talks about self-propelled chairs called “tripods” and golden
“attendants” constructed by Hephaistos, the lame blacksmith god, to help him
get around.1∗
And, in the ancient Greek myth as retold by Ovid in his
Metamorphoses, Pygmalian sculpts an ivory statue of a beautiful maiden,
Galatea, which Venus brings to life:2
The girl felt the kisses he gave, blushed, and, raising her bashful
eyes to the light, saw both her lover and the sky.
The ancient Greek philosopher Aristotle (384–322 bce) dreamed of
automation also, but apparently he thought it an impossible fantasy – thus
making slavery necessary if people were to enjoy leisure. In his The Politics,
he wrote3
For suppose that every tool we had could perform its task, either
at our bidding or itself perceiving the need, and if – like. . . the
tripods of Hephaestus, of which the poet [that is, Homer] says that
“self-moved they enter the assembly of gods” – shuttles in a loom
could fly to and fro and a plucker [the tool used to pluck the
strings] play a lyre of their own accord, then master craftsmen
would have no need of servants nor masters of slaves.
∗So as not to distract the general reader unnecessarily, numbered notes containing citations
to source materials appear at the end of each chapter. Each of these is followed by the number
of the page where the reference to the note occurred.
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![1 Dreams and Dreamers
Web site describing the play,13
“Robots remember everything, and think of
nothing new. According to Domin [the factory director] ‘They’d make fine
university professors.’ . . . once in a while, a Robot will throw down his work
and start gnashing his teeth. The human managers treat such an event as
evidence of a product defect, but Helena [who wants to liberate the Robots]
prefers to interpret it as a sign of the emerging soul.”
I won’t reveal the ending except to say that C̆apek did not look eagerly
on technology. He believed that work is an essential element of human life.
Writing in a 1935 newspaper column (in the third person, which was his habit)
he said: “With outright horror, he refuses any responsibility for the thought
that machines could take the place of people, or that anything like life, love, or
rebellion could ever awaken in their cogwheels. He would regard this somber
vision as an unforgivable overvaluation of mechanics or as a severe insult to
life.”14
Figure 1.4: A scene from a New York production of R.U.R.
There is an interesting story, written by C̆apek himself, about how he
came to use the word robot in his play. While the idea for the play “was still
warm he rushed immediately to his brother Josef, the painter, who was
standing before an easel and painting away. . . . ‘I don’t know what to call
these artificial workers,’ he said. ‘I could call them Labori, but that strikes me
24
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![1.0 NOTES
as a bit bookish.’ ‘Then call them Robots,’ the painter muttered, brush in
mouth, and went on painting.”15
The science fiction (and science fact) writer Isaac Asimov wrote many
stories about robots. His first collection, I, Robot, consists of nine stories
about “positronic” robots.16
Because he was tired of science fiction stories in
which robots (such as Frankenstein’s creation) were destructive, Asimov’s
robots had “Three Laws of Robotics” hard-wired into their positronic brains.
The three laws were the following:
First Law: A robot may not injure a human being, or, through inaction,
allow a human being to come to harm.
Second Law: A robot must obey the orders given it by human beings
except where such orders would conflict with the First Law.
Third Law: A robot must protect its own existence as long as such
protection does not conflict with the First or Second Law.
Asimov later added a “zeroth” law, designed to protect humanity’s interest:17
Zeroth Law: A robot may not injure humanity, or, through inaction, allow
humanity to come to harm.
The quest for artificial intelligence, quixotic or not, begins with dreams
like these. But to turn dreams into reality requires usable clues about how to
proceed. Fortunately, there were many such clues, as we shall see.
Notes
1. The Iliad of Homer, translated by Richmond Lattimore, p. 386, Chicago: The
University of Chicago Press, 1951. (Paperback edition, 1961.) [19]
2. Ovid, Metamorphoses, Book X, pp. 243–297, from an English translation, circa 1850.
See http://www.pygmalion.ws/stories/ovid2.htm. [19]
3. Aristotle, The Politics, p. 65, translated by T. A. Sinclair, London: Penguin Books,
1981. [19]
4. See E. Allison Peers, Fool of Love: The Life of Ramon Lull, London: S. C. M. Press,
Ltd., 1946. [20]
5. See http://en.wikipedia.org/wiki/Leonardo’s robot. [20]
6. Thomas Hobbes, The Leviathon, paperback edition, Kessinger Publishing, 2004. [20]
7. George B. Dyson, Darwin Among the Machines: The Evolution of Global Intelligence,
p. 7, Helix Books, 1997. [21]
8. For a Web site devoted to automata and music boxes, see
http://www.automates-anciens.com/english version/frames/english frames.htm. [21]
9. From Jacques de Vaucanson, “An account of the mechanism of an automaton, or image
playing on the German-flute: as it was presented in a memoire, to the gentlemen of the
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![1 NOTES
Royal-Academy of Sciences at Paris. By M. Vaucanson . . . Together with a description of an
artificial duck. . . . .” Translated out of the French original, by J. T. Desaguliers, London,
1742. Available at http://e3.uci.edu/clients/bjbecker/NatureandArtifice/week5d.html. [21]
10. http://www.automates-anciens.com/english version/automatons-music-boxes/
vaucanson-automatons-androids.php. [22]
11. de Vaucanson, Jacques, op. cit. [22]
12. I thank Prof. Barbara Becker of the University of California at Irvine for telling me about
the automates-anciens.com Web sites. [22]
13. http://jerz.setonhill.edu/resources/RUR/index.html. [24]
14. For a translation of the column entitled “The Author of Robots Defends Himself,” see
http://www.depauw.edu/sfs/documents/capek68.htm. [24]
15. From one of a group of Web sites about C̆apek,
http://Capek.misto.cz/english/robot.html. See also http://Capek.misto.cz/english/. [25]
16. The Isaac Asimov Web site, http://www.asimovonline.com/, claims that “Asimov did
not come up with the title, but rather his publisher ‘appropriated’ the title from a short
story by Eando Binder that was published in 1939.” [25]
17. See http://www.asimovonline.com/asimov FAQ.html#series13 for information about
the history of these four laws. [25]
26
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![2 Clues
Figure 2.3: George Boole.
Boolean algebra plays an important role in the design of telephone
switching circuits and computers. Although Boole probably could not have
envisioned computers, he did realize the importance of his work. In a letter
dated January 2, 1851, to George Thomson (later Lord Kelvin) he wrote7
I am now about to set seriously to work upon preparing for the
press an account of my theory of Logic and Probabilities which in
its present state I look upon as the most valuable if not the only
valuable contribution that I have made or am likely to make to
Science and the thing by which I would desire if at all to be
remembered hereafter. . .
Boole’s work showed that some kinds of logical reasoning could be
performed by manipulating equations representing logical propositions – a
very important clue about the mechanization of reasoning. An essentially
equivalent, but not algebraic, system for manipulating and evaluating
propositions is called the “propositional calculus” (often called “propositional
logic”), which, as we shall see, plays a very important role in artificial
intelligence. [Some claim that the Greek Stoic philospher Chrysippus (280–209
bce) invented an early form of the propositional calculus.8
]
32
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![2 Clues
This development might at first be regarded as a step backward for people
wanting to get useful clues about the internal workings of the brain. In
criticizing the statistically oriented theories arising from “behaviorism,”
Marvin Minsky wrote “Originally intended to avoid the need for ‘meaning,’
[these theories] manage finally only to avoid the possibility of explaining it.”14
Skinner’s work did, however, provide the idea of a reinforcing stimulus – one
that rewards recent behavior and tends to make it more likely to occur (under
similar circumstances) in the future.
Reinforcement learning has become a popular strategy among AI
researchers, although it does depend on internal states. Russell Kirsch (circa
1930– ), a computer scientist at the U.S. National Bureau of Standards (now
the National Institute for Standards and Technology, NIST), was one of the
first to use it. He proposed how an “artificial animal” might use reinforcement
to learn good moves in a game. In some 1954 seminar notes he wrote the
following:15
“The animal model notes, for each stimulus, what move the
opponent next makes, . . . Then, the next time that same stimulus occurs, the
animal duplicates the move of the opponent that followed the same stimulus
previously. The more the opponent repeats the same move after any given
stimulus, the more the animal model becomes ‘conditioned’ to that move.”
Skinner believed that reinforcement learning could even be used to
explain verbal behavior in humans. He set forth these ideas in his 1957 book
Verbal Behavior,16
claiming that the laboratory-based principles of selection
by consequences can be extended to account for what people say, write,
gesture, and think.
Arguing against Skinner’s ideas about language the linguist Noam
Chomsky (1928– ; Fig. 2.10), in a review17
of Skinner’s book, wrote that
careful study of this book (and of the research on which it draws)
reveals, however, that [Skinner’s] astonishing claims are far from
justified. . . . the insights that have been achieved in the
laboratories of the reinforcement theorist, though quite genuine,
can be applied to complex human behavior only in the most gross
and superficial way, and that speculative attempts to discuss
linguistic behavior in these terms alone omit from consideration
factors of fundamental importance. . .
How, Chomsky seems to ask, can a person produce a potentially infinite
variety of previously unheard and unspoken sentences having arbitrarily
complex structure (as indeed they can do) through experience alone? These
“factors of fundamental importance” that Skinner omits are, according to
Chomsky, linguistic abilities that must be innate – not learned. He suggested
that “human beings are somehow specially created to do this, with
data-handling or ‘hypothesis-formulating’ ability of [as yet] unknown character
and complexity.” Chomsky claimed that all humans have at birth a “universal
40
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![2 Clues
memory.
Miller’s paper on “The Magical Number Seven,” was given at a
Symposium on Information Theory held from September 10 to 12, 1956, at
MIT.20
Chomsky presented an important paper there too. It was entitled
“Three Models for the Description of Language,” and in it he proposed a
family of rules of syntax he called phrase-structure grammars.21
It happens
that two pioneers in AI research (of whom we’ll hear a lot more later), Allen
Newell (1927–1992), then a scientist at the Rand Corporation, and Herbert
Simon (1916–2001), a professor at the Carnegie Institute of Technology (now
Carnegie Mellon University), gave a paper there also on a computer program
that could prove theorems in propositional logic. This symposium, bringing
together as it did scientists with these sorts of overlapping interests, is thought
to have contributed to the birth of cognitive science, a new discipline devoted
to the study of the mind. Indeed, George Miller wrote22
I went away from the Symposium with a strong conviction, more
intuitive than rational, that human experimental psychology,
theoretical linguistics, and computer simulation of cognitive
processes were all pieces of a larger whole, and that the future
would see progressive elaboration and coordination of their shared
concerns. . .
In 1960, Miller and colleagues wrote a book proposing a specific internal
mechanism responsible for behavior, which they called the TOTE unit
(Test–Operate–Test–Exit).23
There is a TOTE unit corresponding to every
goal that an agent might have. Using its perceptual abilities, the unit first
tests whether or not its goal is satisfied. If so, the unit rests (exits). If not,
some operation specific to achieving that goal is performed, and the test for
goal achievement is performed again, and so on repetitively until the goal
finally is achieved. As a simple example, consider the TOTE unit for driving a
nail with a hammer. So long as the nail is not completely driven in (the goal),
the hammer is used to strike it (the operation). Pounding stops (the exit)
when the goal is finally achieved. It’s difficult to say whether or not this book
inspired similar work by artificial intelligence researchers. The idea was
apparently “in the air,” because at about the same time, as we shall see later,
some early work in AI used very similar ideas. [I can say that my work at SRI
with behavior (intermediate-level) programs for the robot, Shakey, and my
later work on what I called “teleo-reactive” programs were influenced by
Miller’s ideas.]
Cognitive science attempted to explicate internal mental processes using
ideas such as goals, memory, task queues, and strategies without (at least
during its beginning years) necessarily trying to ground these processes in
neurophysiology.24
Cognitive science and artificial intelligence have been
closely related ever since their beginnings. Cognitive science has provided
clues for AI researchers, and AI has helped cognitive science with newly
42
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![2.2 From Life Itself
invented concepts useful for understanding the workings of the mind.
2.2.3 Evolution
That living things evolve gives us two more clues about how to build
intelligent artifacts. First, and most ambitiously, the processes of evolution
itself – namely, random generation and selective survival – might be simulated
on computers to produce the machines we dream about. Second, those paths
that evolution followed in producing increasingly intelligent animals can be
used as a guide for creating increasingly intelligent artifacts. Start by
simulating animals with simple tropisms and proceed along these paths to
simulating more complex ones. Both of these strategies have been followed
with zest by AI researchers, as we shall see in the following chapters. Here, it
will suffice to name just a few initial efforts.
Early attempts to simulate evolution on a computer were undertaken at
Princeton’s Institute for Advanced Study by the viral geneticist Nils Aall
Barricelli (1912–1993). His 1954 paper described experiments in which
numbers migrated and reproduced in a grid.25
Motivated by the success of biological evolution in producing complex
organisms, some researchers began thinking about how programs could be
evolved rather than written. R. N. Friedberg and his IBM colleagues26
conducted experiments in which, beginning with a population of random
computer programs, they attempted to evolve ones that were more successful
at performing a simple logical task. In the summary of his 1958 paper,
Friedberg wrote that “[m]achines would be more useful if they could learn to
perform tasks for which they were not given precise methods. . . . It is
proposed that the program of a stored-program computer be gradually
improved by a learning procedure which tries many programs and chooses,
from the instructions that may occupy a given location, the one most often
associated with a successful result.” That is, Friedberg installed instructions
from “successful” programs into the programs of the next “generation,” much
as how the genes of individuals successful enough to have descendants are
installed in those descendants.
Unfortunately, Friedberg’s attempts to evolve programs were not very
successful. As Marvin Minsky pointed out,27
The machine [described in the first paper] did learn to solve some
extremely simple problems. But it took of the order of 1000 times
longer than pure chance would expect. . . .
The second paper goes on to discuss a sequence of
modifications. . . With these, and with some ‘priming’ (starting the
machine off on the right track with some useful instructions), the
system came to be only a little worse than chance.
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![2 Clues
from infancy to adolescence.34
Might these stages provide a set of steps that
could guide designers of intelligent artifacts? Start with a machine that is able
to do what an infant can do, and then design machines that can mimic the
abilities of children at each rung of the ladder. This strategy might be called
“ontogenetic” to contrast it with the “phylogenetic” strategy of using
simlulated evolution.
Of course, it may be that an infant mind is far too complicated to
simulate and the processes of its maturation too difficult to follow. In any
case, this particular clue remains to be exploited.
2.2.5 Bionics
At a symposium in 1960, Major Jack E. Steele, of the Aerospace Division of
the United States Air Force, used the term “bionics” to describe the field that
learns lessons from nature to apply to technology.35
Several bionics and bionics-related meetings were held during the 1960s.
At the 1963 Bionics Symposium, Leonard Butsch and Hans Oestreicher wrote
“Bionics aims to take advantage of millions of years of evolution of living
systems during which they adapted themselves for optimum survival. One of
the outstanding successes of evolution is the information processing capability
of living systems [the study of which is] one of the principal areas of Bionics
research.”36
Today, the word “bionics” is concerned mainly with orthotic and
prosthetic devices, such as artificial cochleas, retinas, and limbs. Nevertheless,
as AI researchers continue their quest, the study of living things, their
evolution, and their development may continue to provide useful clues for
building intelligent artifacts.
2.3 From Engineering
2.3.1 Automata, Sensing, and Feedback
Machines that move by themselves and even do useful things by themselves
have been around for centuries. Perhaps the most common early examples are
the “verge-and-foliot” weight-driven clocks. (See Fig. 2.12.) These first
appeared in the late Middle Ages in the towers of large Italian cities. The
verge-and-foliot mechanism converted the energy of a falling weight into
stepped rotational motion, which could be used to move the clock hands.
Similar mechanisms were elaborated to control the actions of automata, such
as those of the Munich Glockenspiel.
One of the first automatic machines for producing goods was
Joseph-Marie Jacquard’s weaving loom, built in 1804. (See Fig. 2.13.) It
46
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![2.3 From Engineering
Inspired by Pascal’s machines, Gottfried Leibniz built a mechanical
multiplier called the “Step Reckoner” in 1674. It could add, subtract, and do
multiplication (by repeated additions). “To multiply a number by 5, one
simply turned the crank five times.”47
Several other calculators were built in the ensuing centuries. A
particularly interesting one, which was too complicated to build in its day, was
designed in 1822 by Charles Babbage (1791–1871), an English mathematician
and inventor. (See Fig. 2.17.) Called the “Difference Engine,” it was to have
calculated mathematical tables (of the kind used in navigation at sea, for
example) using the method of finite differences. Babbage’s Difference Engine
No. 2 was actually constructed in 1991 (using Babbage’s designs and
nineteenth-century mechanical tolerances) and is now on display at the
London Science Museum. The Museum arranged for another copy to be built
for Nathan Myhrvold, a former Microsoft Chief Technology Officer. (A
description of the machine and a movie is available from a Computer History
Museum Web page at http://www.computerhistory.org/babbage/.)
Adding machines, however, can only add and subtract (and, by repetition
of these operations, also multiply and divide). These are important operations
but not the only ones needed. Between 1834 and 1837 Babbage worked on the
design of a machine called the “Analytical Engine,” which embodied most of
the ideas needed for general computation. It could store intermediate results
in a “mill,” and it could be programmed. However, its proposed realization as
a collection of steam-driven, interacting brass gears and cams ran into funding
difficulties and was never constructed.
Ada Lovelace (1815–1852), the daughter of Lord Byron, has been called
the “world’s first programmer” for her alleged role in devising programs for
the Analytical Engine. However, in the book Computing Before Computers
the following claim is made:48
This romantically appealing image is without foundation. All but
one of the programs cited in her notes [to her translation of an
account of a lecture Babbage gave in Turin, Italy] had been
prepared by Babbage from three to seven years earlier. The
exception was prepared by Babbage for her, although she did
detect a “bug” in it. Not only is there no evidence that Ada
Lovelace ever prepared a program for the Analytical Engine but
her correspondence with Babbage shows that she did not have the
knowledge to do so.
For more information about the Analytical Engine and an emulator and
programs for it, see http://www.fourmilab.ch/babbage/.
Practical computers had to await the invention of electrical, rather than
brass, devices. The first computers in the early 1940s used electromechanical
relays. Vacuum tubes (thermionic valves, as they say in Britain) soon won out
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![2.3 From Engineering
We call computers “machines” even though today they can be made
completely electrical with no moving parts whatsoever. Furthermore, when we
speak of computing machines we usually mean the combination of the
computer and the program it is running. Sometimes we even call just the
program a machine. (As an example of this usage, I’ll talk later about a
“checker-playing machine” and mean a program that plays checkers.)
The commanding importance of the stored-program digital computer
derives from the fact that it can be used for any purpose whatsoever – that is,
of course, any computational purpose. The modern digital computer is, for all
practical purposes, such a universal machine. The “all-practical-purposes”
qualifier is needed because not even modern computers have the infinite
storage capacity implied by Turing’s infinite tape. However, they do have
prodigious amounts of storage, and that makes them practically universal.
D. “Thinking” Computers
After some of the first computers were built, Turing reasoned that if they were
practically universal, they should be able to do anything. In 1948 he wrote,
“The importance of the universal machine is clear. We do not need to have an
infinity of different machines doing different jobs. A single one will suffice. The
engineering problem of producing various machines for various jobs is replaced
by the office work of ‘programming’ the universal machine to do these jobs.”57
Among the things that Turing thought could be done by computers was
mimicking human intelligence. One of Turing’s biographers, Andrew Hodges,
claims, “he decided the scope of the computable encompassed far more than
could be captured by explicit instruction notes, and quite enough to include all
that human brains did, however creative or original. Machines of sufficient
complexity would have the capacity for evolving into behaviour that had never
been explicitly programmed.”58
The first modern article dealing with the possibility of mechanizing all of
human-style intelligence was published by Turing in 1950.59
This paper is
famous for several reasons. First, Turing thought that the question “Can a
machine think?” was too ambiguous. Instead he proposed that the matter of
machine intelligence be settled by what has come to be called “the Turing
test.”
Although there have been several reformulations (mostly simplifications)
of the test, here is how Turing himself described it:
The new form of the problem [Can machines think?] can be
described in terms of a game which we call the “imitation game.”
It is played with three people, a man (A), a woman (B), and an
interrogator (C) who may be of either sex. The interrogator stays
in a room apart from the other two. The object of the game for the
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![2 NOTES
Newell and Simon admitted that their hypothesis could indeed be false:
“Intelligent behavior is not so easy to produce that any system will exhibit it
willy-nilly. Indeed, there are people whose analyses lead them to conclude
either on philosophical or on scientific grounds that the hypothesis is false.
Scientifically, one can attack or defend it only by bringing forth empirical
evidence about the natural world.” They conclude the following:
The symbol system hypothesis implies that the symbolic behavior
of man arises because he has the characteristics of a physical
symbol system. Hence, the results of efforts to model human
behavior with symbol systems become an important part of the
evidence for the hypothesis, and research in artificial intelligence
goes on in close collaboration with research in information
processing psychology, as it is usually called.
Although the hypothesis was not formally described until it appeared in
the 1976 article, it was certainly implicit in what Turing and other researchers
believed in the 1950s. After Allen Newell’s death, Herb Simon wrote, “From
the very beginning something like the physical symbol system hypothesis was
embedded in the research.”64
Inspired by the clues we have mentioned and armed with the
general-purpose digital computer, researchers began, during the 1950s, to
explore various paths toward mechanizing intelligence. With a firm belief in
the symbol system hypothesis, some people began programming computers to
attempt to get them to perform some of the intellectual tasks that humans
could perform. Around the same time, other researchers began exploring
approaches that did not depend explicitly on symbol processing. They took
their inspiration mainly from the work of McCulloch and Pitts on networks of
neuron-like units and from statistical approaches to decision making. A split
between symbol-processing methods and what has come to be called
“brain-style” and “nonsymbolic” methods still survives today.
Notes
1. Aristotle, Prior Analytics, Book I, written circa 350 bce, translated by A. J. Jenkinson,
Web addition published by eBooks@Adelaide, available online at
http://etext.library.adelaide.edu.au/a/aristotle/a8pra/. [27]
2. Medieval students of logic gave names to the different syllogisms they studied. They
used the mnemonic Barbara for this one because each of the three statements begins with
“All,” whose first letter is “A.” The vowels in “Barbara” are three“a”s. [27]
3. From Martin Davis, The Universal Computer: The Road from Leibniz to Turing, New
York: W. W. Norton & Co., 2000. For an excerpt from the paperback version containing
this quotation, see http://www.wwnorton.com/catalog/fall01/032229EXCERPT.htm. [28]
4. Quotation from William Aspray (ed.), Computing Before Computers, Chapter 3, “Logic
66
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![2.3 NOTES
Machines,” pp. 107–8, Ames, Iowa: Iowa State Press, 1990. (Also available from
http://ed-thelen.org/comp-hist/CBC.html.) [30]
5. Robert Harley, “The Stanhope Demonstrator, Mind, Vol. IV, pp. 192–210, 1879. [31]
6. George Boole, An Investigation of the Laws of Thought on Which are Founded the
Mathematical Theories of Logic and Probabilities, Dover Publications, 1854. [31]
7. See D. McHale, George Boole: His Life and Work, Dublin, 1985. This excerpt was taken
from http://www-groups.dcs.st-and.ac.uk/∼history/Mathematicians/Boole.html. [32]
8. See, for example, Gerard O’Regan, A Brief History of Computing, p. 17, London:
Springer-Verlag, 2008. [32]
9. I follow the pictorial version used in the online Stanford Encyclopedia of Philosophy
(http://plato.stanford.edu/entries/frege/), which states that “. . . we are modifying Frege’s
notation a bit so as to simplify the presentation; we shall not use the special typeface
(Gothic) that Frege used for variables in general statements, or observe some of the special
conventions that he adopted. . . .” [33]
10. Warren S. McCulloch and Walter Pitts, “A Logical Calculus of Ideas Immanent in
Nervous Activity,” Bulletin of Mathematical Biophysics, Vol. 5, pp. 115–133, Chicago:
University of Chicago Press, 1943. (See Marvin Minsky, Computation: Finite and Infinite
Machines, Englewood Cliffs, NJ: Prentice-Hall, 1967, for a very readable treatment of the
computational aspects of “McCulloch–Pitts neurons.”) [34]
11. Donald O. Hebb, The Organization of Behavior: A Neuropsychological Theory, New
York: John Wiley, Inc., 1949. [36]
12. For more about Hebb, see http://www.cpa.ca/Psynopsis/special eng.html. [36]
13. For a summary of the lives and work of both men, see a Web page entitled “Wilhelm
Wundt and William James” by Dr. C. George Boeree at
http://www.ship.edu/∼cgboeree/wundtjames.html. [38]
14. M. Minsky (ed.), “Introduction,” Semantic Information Processing, p. 2, Cambridge,
MA: MIT Press, 1968. [40]
15. Russell A. Kirsch, “Experiments with a Computer Learning Routine,” Computer
Seminar Notes, July 30, 1954. Available online at
http://www.nist.gov/msidlibrary/doc/kirsch 1954 artificial.pdf. [40]
16. B. F. Skinner, Verbal Behavior, Engelwood Cliffs, NJ: Prentice Hall, 1957. [40]
17. Noam Chomsky, “A Review of B. F. Skinner’s Verbal Behavior,” in Leon A. Jakobovits
and Murray S. Miron (eds.), Readings in the Psychology of Language, Engelwood Cliffs, NJ:
Prentice-Hall, 1967. Available online at http://www.chomsky.info/articles/1967----.htm.
[40]
18. See, for example, N. Chomsky, Aspects of the Theory of Syntax, Cambridge: MIT Press,
1965. [41]
19. George A. Miller, “The Magical Number Seven, Plus or Minus Two: Some Limits on
Our Capacity for Processing Information,” The Psychological Review, Vol. 63, pp. 81–97,
1956. [41]
20. IRE Transactions on Information Theory, Vol IT-2, 1956. [42]
21. For a copy of his paper, see http://www.chomsky.info/articles/195609--.pdf. [42]
22. George A. Miller, “A Very Personal History,” MIT Center for Cognitive Science
Occasional Paper No. 1, 1979. [42]
23. George A. Miller, E. Galanter, and K. H. Pribram, Plans and the Structure of Behavior,
New York: Holt, Rinehart & Winston, 1960. [42]
24. For a thorough history of cognitive science, see Margaret A. Boden, Mind As Machine:
Copyright c 2010 Nils J. Nilsson
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![2 NOTES
A History of Cognitive Science, vols. 1 and 2, Oxford: Clarendon Press, 2006. For an
earlier, one-volume treatment, see Howard E. Gardner, The Mind’s New Science: A History
of the Cognitive Revolution, New York: Basic Books, 1985. [42]
25. An English translation appeared later: N.A. Barricelli, “Symbiogenetic Evolution
Processes Realized by Artificial Methods,” Methodos, Vol. 9, Nos. 35–36, pp. 143–182, 1957.
For a summary of Barricelli’s experiments, see David B. Fogel, “Nils Barricelli – Artificial
Life, Coevolution, Self-Adaptation,” IEEE Computational Intelligence Magazine, Vol. 1, No.
1, pp. 41–45, February 2006. [43]
26. R. M. Friedberg, “A Learning Machine: Part I,” IBM Journal of Research and
Development, Vol. 2, No. 1, pp. 2–13, 1958, and R. M. Friedberg, B. Dunham, and J. H.
North, “A Learning Machine: Part II,” IBM Journal of Research and Development, Vol. 3,
No. 3, pp. 282–287, 1959. The papers are available (for a fee) at
http://www.research.ibm.com/journal/rd/021/ibmrd0201B.pdf and
http://www.research.ibm.com/journal/rd/033/ibmrd0303H.pdf. [43]
27. Marvin L. Minsky, “Steps Toward Artificial Intelligence,” Proceedings of the Institute of
Radio Engineers, Vol. 49, pp. 8–30, 1961. Paper available at
http://web.media.mit.edu/∼minsky/papers/steps.html. [43]
28. Lawrence J. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence through
Simulated Evolution, New York: Wiley, 1966. [44]
29. Woodrow W. Bledsoe, “The Evolutionary Method in Hill Climbing: Convergence
Rates,” Technical Report, Panoramic Research, Inc., Palo Alto, CA, 1962.; Hans J.
Bremermann, “Optimization through Evolution and Recombination, M. C. Yovits, G. T.
Jacobi, and G. D. Goldstein (eds.), Self-Organizing Systems, pp. 93–106, Washington, DC:
Spartan Books, 1962. [44]
30. Jürgen Schmidhuber, “2006: Celebrating 75 Years of AI – History and Outlook: The
Next 25 Years,” in Max Lungarella et al. (eds.), 50 Years of Artificial Intelligence: Essays
Dedicated to the 50th Anniversary of Artificial Intelligence, Berlin: Springer-Verlag, 2007.
Schmiduber cites Ingo Rechenberg, “Evolutionsstrategie – Optimierung Technischer Systeme
Nach Prinzipien der Biologischen Evolution,” Ph.D. dissertation, 1971 (reprinted by
Frommann-Holzboog Verlag, Stuttgart, 1973). [44]
31. See http://www.aaai.org/AITopics/html/genalg.html. [44]
32. John H. Holland, Adaptation in Natural and Artificial Systems, Ann Arbor: The
University of Michigan Press, 1975. Second edition, MIT Press, 1992. [44]
33. W. Grey Walter, “An Imitation of Life,” Scientific American, pp. 42–45, May 1950. See
also W. Grey Walter, The Living Brain, London: Gerald Duckworth & Co. Ltd., 1953. [44]
34. B. Inhelder and J. Piaget, The Growth of Logical Thinking from Childhood to
Adolescence, New York: Basic Books, 1958. For a summary of these stages, see the following
Web pages: http://www.childdevelopmentinfo.com/development/piaget.shtml and
http://www.ship.edu/∼cgboeree/piaget.html. [46]
35. Proceedings of the Bionics Symposium: Living Prototypes – the Key to new Technology,
Technical Report 60-600, Wright Air Development Division, Dayton, Ohio, 1960. [46]
36. Proceedings of the Third Bionics Symposium, Aerospace Medical Division, Air Force
Systems Command, United States Air Force, Wright-Patterson AFB, Ohio, 1963. [46]
37. http://www.mlahanas.de/Greeks/Ctesibius1.htm. [49]
38. http://www.asc-cybernetics.org/foundations/timeline.htm. [49]
39. From http://www.nickgreen.pwp.blueyonder.co.uk/control.htm. [49]
40. For a history of cybernetics, see a Web page of the American Society for Cybernetics at
http://www.asc-cybernetics.org/foundations/history.htm. [50]
68
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![2.3 NOTES
41. From http://pespmc1.vub.ac.be/ASC/SELF-ORGANI.html. [51]
42. W. Ross Ashby, “Principles of the Self-Organizing Dynamic System,” Journal of
General Psychology, Vol. 37, pp. 125–128, 1947. See also the Web pages at
http://en.wikipedia.org/wiki/Self organization. [51]
43. Bayes wrote an essay that is said to have contained a version of the rule. Later, the
Marquis de Laplace (1749–1827) generalized (some say independently) what Bayes had
done. For a version of Bayes’s essay (posthumously written up by Richard Price), see
http://www.stat.ucla.edu/history/essay.pdf. [52]
44. David Van Meter and David Middleton, “Modern Statistical Approaches to Reception in
Communication Theory,” Symposium on Information Theory, IRE Transactions on
Information Theory, PGIT-4, pp. 119–145, September 1954. [53]
45. William Aspray (ed.), Computing Before Computers, Ames, Iowa: Iowa State University
Press, 1990. Available online at http://ed-thelen.org/comp-hist/CBC.html. [54]
46. Ibid, Chapter 1. [54]
47. Ibid. [55]
48. Ibid, Chapter 2. [55]
49. Alonzo Church, “An Unsolvable Problem of Elementary Number Theory,” American
Journal of Mathematics, Vol. 58, pp. 345–363, 1936. [56]
50. Alan M. Turing, “On Computable Numbers, with an Application to the
Entscheidungsproblem,” Proceedings of the London Mathematical Society, Series 2, Vol. 42,
pp. 230–265, 1936–1937. [56]
51. For more information about Turing, his life and works, see the Web pages maintained by
the Turing biographer, Andrew Hodges, at http://www.turing.org.uk/turing/. [56]
52. The quotation is from Alan M. Turing, “Lecture to the London Mathematical Society,”
p. 112, typescript in King’s College, Cambridge, published in Alan M. Turing’s ACE Report
of 1946 and Other Papers (edited by B. E. Carpenter and R. W. Doran, Cambridge, MA:
MIT Press, 1986), and in Volume 3 of The Collected Works of A. M. Turing (edited D. C.
Ince, Amsterdam: North-Holland 1992). [58]
53. For a biographical sketch, see
http://www.research.att.com/∼njas/doc/shannonbio.html. [58]
54. In his book The Mind’s New Science, Howard Gardner called this thesis “possibly the
most important, and also the most famous, master’s thesis of the century.” [58]
55. Various sources give different dates for the air raid, but a letter in the possession of
Zuse’s son, Horst Zuse, gives the 1943 date (according to an e-mail sent me on February 10,
2009, by Wolfgang Bibel, who has communicated with Horst Zuse). [59]
56. A copy of the report, plus introductory commentary, can be found at
http://qss.stanford.edu/∼godfrey/. [60]
57. Alan M. Turing, “Intelligent Machinery,” National Physical Laboratory Report, 1948.
Reprinted in B. Meltzer and D. Michie (eds), Machine Intelligence 5, Edinburgh: Edinburgh
University Press, 1969. A facsimile of the report is available online at
http://www.AlanTuring.net/intelligent machinery. [61]
58. Andrew Hodges, Turing, London: Phoenix, 1997. [61]
59. Alan M. Turing, “Computing Machinery and Intelligence,” Mind, Vol. LIX, No. 236, pp.
433–460, October 1950. (Available at http://www.abelard.org/turpap/turpap.htm.) [61]
60. See the “Home Page of the Loebner Prize in Artificial Intelligence” at
http://www.loebner.net/Prizef/loebner-prize.html. [62]
61. For discussion, see the Wikipedia article at http://en.wikipedia.org/wiki/Turing test.
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![2 NOTES
[62]
62. Joseph Weizenbaum, “ELIZA—A Computer Program for the Study of Natural
Language Communication between Man and Machine,” Communications of the ACM, Vol.
9, No. 1, pp. 36–35, January 1966. Available online at
http://i5.nyu.edu/∼mm64/x52.9265/january1966.html. [62]
63. Allen Newell and Herbert A. Simon, “Computer Science as Empirical Inquiry: Symbols
and Search,” Communications of the ACM, Vol. 19, No. 3, pp. 113–126, March 1976. [64]
64. National Academy of Sciences, Biographical Memoirs, Vol. 71, 1997. Available online at
http://www.nap.edu/catalog.php?record id=5737. [66]
70
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![3.1 Session on Learning Machines
and go after it.
Selfridge’s paper5
was a companion piece to that of Dinneen. Operating
on “cleaned-up” images (as might be produced by Dinneen’s program, for
example), Selfridge described techniques for highlighting “features” in these
images and then classifying them based on the features. For example, corners
of an image known to be either a square or a triangle are highlighted, and then
the number of corners is counted to determine whether the image is of a
square or of a triangle. Selfridge said that “eventually, we hope to be able to
recognize other kinds of features, such as curvature, juxtaposition of singular
points (that is, their relative bearings and distances), and so forth.”
Figure 3.1: Oliver Selfridge. (Photograph courtesy of Oliver Selfridge.)
The methods pioneered by Selfridge and Dinneen are fundamental to
most of the later work in enabling machines to “see.” Their work is all the
more remarkable when one considers that it was done on a computer, the
Lincoln Laboratory “Memory Test Computer,” that today would be regarded
as extremely primitive. [The Memory Test Computer (MTC) was the first to
use the ferrite core random-access memory modules developed by Jay
Forrester. It was designed and built by Ken Olsen in 1953 at the Digital
Equipment Corporation (DEC). The MTC was the first computer to simulate
the operation of neural networks – those of Clark and Farley.]
The next paper6
was about programming a computer to play chess. It
was written by Allen Newell, then a researcher at the Rand Corporation in
Santa Monica. Thanks to a biographical sketch of Newell written by his
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![3 Gatherings
colleague, Herb Simon of Carnegie Mellon University, we know something
about Newell’s motivation and how he came to be interested in this problem:7
In September 1954 Allen attended a seminar at RAND in which
Oliver Selfridge of Lincoln Laboratory described a running
computer program that learned to recognize letters and other
patterns. While listening to Selfridge characterizing his rather
primitive but operative system, Allen experienced what he always
referred to as his “conversion experience.” It became instantly clear
to him “that intelligent adaptive systems could be built that were
far more complex than anything yet done.” To the knowledge Allen
already had about computers (including their symbolic
capabilities), about heuristics, about information processing in
organizations, about cybernetics, and proposals for chess programs
was now added a concrete demonstration of the feasibility of
computer simulation of complex processes. Right then he
committed himself to understanding human learning and thinking
by simulating it.
Simon goes on to summarize Newell’s paper on chess:
[It] outlined an imaginative design for a computer program to play
chess in humanoid fashion, incorporating notions of goals,
aspiration levels for terminating search, satisfying with “good
enough” moves, multidimensional evaluation functions, the
generation of subgoals to implement goals, and something like best
first search. Information about the board was to be expressed
symbolically in a language resembling the predicate calculus. The
design was never implemented, but ideas were later borrowed from
it for use in the NSS [Newell, Shaw, and Simon] chess program in
1958.8
Newell hinted that his aims extended beyond chess. In his paper he wrote
“The aim of this effort, then, is to program a current computer to learn to
play good chess. This is the means to understanding more about the kinds of
computers, mechanisms, and programs that are necessary to handle
ultracomplicated problems.” Newell’s proposed techniques can be regarded as
his first attempt to produce evidence for what he and Simon later called the
Physical Symbol System Hypothesis.
Walter Pitts, a commentator for this session, concluded it by saying,
“But, whereas Messrs. Farley, Clark, Selfridge, and Dinneen are imitating the
nervous system, Mr. Newell prefers to imitate the hierarchy of final causes
traditionally called the mind. It will come to the same thing in the end, no
doubt. . . .” To “come to the same thing,” these two approaches, neural
modeling and symbol processing, must be recognized simply as different levels
76
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![3.2 The Dartmouth Summer Project
There was (and still is) controversy surrounding the name. According to
Pamela McCorduck’s excellent history of the early days of artificial
intelligence, Art Samuel remarked, “The word artificial makes you think
there’s something kind of phony about this, or else it sounds like it’s all
artificial and there’s nothing real about this work at all.”13
McCorduck goes
on to say that “[n]either Newell or Simon liked the phrase, and called their
own work complex information processing for years thereafter.” But most of
the people who signed on to do work in this new field (including myself) used
the name “artificial intelligence,” and that is what the field is called today.
(Later, Newell became reconciled to the name. In commenting about the
content of the field, he concluded, “So cherish the name artificial intelligence.
It is a good name. Like all names of scientific fields, it will grow to become
exactly what its field comes to mean.”)14
The approaches and motivations of the people at the workshop differed.
Rochester came to the conference with a background in networks of neuron-like
elements. Newell and Simon had been pursuing (indeed had helped originate)
the symbol-processing approach. Among the topics Shannon wanted to think
about (according to the proposal) was the “application of information theory
concepts to computing machines and brain models.” (After the workshop,
however, Shannon turned his attention away from artificial intelligence.)
McCarthy wrote that he was interested in constructing “an artificial
language which a computer can be programmed to use on problems requiring
conjecture and self-reference. It should correspond to English in the sense that
short English statements about the given subject matter should have short
correspondents in the language and so should short arguments or conjectural
arguments. I hope to try to formulate a language having these properties . . . ”
Although McCarthy later said that his ideas on this topic were still too “ill
formed” for presentation at the conference, it was not long before he made
specific proposals for using a logical language and its inference mechanisms for
representing and reasoning about knowledge.
Although Minsky’s Ph.D. dissertation15
and some of his subsequent work
concentrated on neural nets, around the time of the Dartmouth workshop he
was beginning to change direction. Now, he wrote, he wanted to consider a
machine that “would tend to build up within itself an abstract model of the
environment in which it is placed. If it were given a problem, it could first
explore solutions within the internal abstract model of the environment and
then attempt external experiments.” At the workshop, Minsky continued work
on a draft that was later to be published as a foundational paper, “Steps
Toward Artificial Intelligence.”16
One of the most important technical contributions of the 1956 meeting
was work presented by Newell and Simon on their program, the “Logic
Theorist (LT),” for proving theorems in symbolic logic. LT was concrete
evidence that processing “symbol structures” and the use of what Newell and
Simon called “heuristics” were fundamental to intelligent problem solving. I’ll
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![3 Gatherings
describe some of these ideas in more detail in a subsequent chapter.
Newell and Simon had been working on ideas for LT for some months and
became convinced in late 1955 that they could be embodied in a working
program. According to Edward Feigenbaum (1936– ), who was taking a course
from Herb Simon at Carnegie in early 1956, “It was just after Christmas
vacation – January 1956 – when Herb Simon came into the classroom and
said, ‘Over Christmas Allen Newell and I invented a thinking machine.’”17
What was soon to be programmed as LT was the “thinking machine” Simon
was talking about. He called it such, no doubt, because he thought it used
some of the same methods for solving problems that humans use. Simon later
wrote18
“On Thursday, Dec. 15. . . I succeeded in simulating by hand the first
proof. . . I have always celebrated Dec. 15, 1955, as the birthday of heuristic
problem solving by computer.” According to Simon’s autobiography Models of
My Life,19
LT began by hand simulation, using his children as the computing
elements, while writing on and holding up note cards as the registers that
contained the state variables of the program.20
Another topic discussed at Dartmouth was the problem of proving
theorems in geometry. (Perhaps some readers will recall their struggles with
geometry proofs in high school.) Minsky had already been thinking about a
program to prove geometry theorems. McCorduck quotes him as saying the
following:21
[P]robably the important event in my own development – and the
explanation of my perhaps surprisingly casual acceptance of the
Newell–Shaw–Simon work – was that I had sketched out the
heuristic search procedure for [a] geometry machine and then been
able to hand-simulate it on paper in the course of an hour or so.
Under my hand the new proof of the isosceles-triangle theorem
came to life, a proof that was new and elegant to the participants –
later, we found that proof was well-known. . .
In July 2006, another conference was held at Dartmouth celebrating the
fiftieth anniversary of the original conference. (See Fig. 3.3.) Several of the
founders and other prominent AI researchers attended and surveyed what had
been achieved since 1956. McCarthy reminisced that the “main reason the
1956 Dartmouth workshop did not live up to my expectations is that AI is
harder than we thought.” In any case, the 1956 workshop is considered to be
the official beginning of serious work in artificial intelligence, and Minsky,
McCarthy, Newell, and Simon came to be regarded as the “fathers” of AI. A
plaque was dedicated and installed at the Baker Library at Dartmouth
commemorating the beginning of artificial intelligence as a scientific discipline.
80
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![3 NOTES
1. The proceedings of the symposium were published in L. A. Jeffries (ed.), Cerebral
Mechanisms in Behavior: The Hixon Symposium, New York: Wiley, 1951. An excellent
review of Lashley’s points are contained in Chapter 2 of The Mind’s New Science: A History
of the Cognitive Revolution, by Howard E Gardner, New York: Basic Books, 1985. [73]
2. W. A. Clark and B. G. Farley, “Generalization of Pattern Recognition in a
Self-Organizing System,” Proceedings of the 1955 Western Joint Computer Conference,
Institute of Radio Engineers, New York, pp. 86–91, 1955. Clark and Farley’s experiments
continued some work they had reported on earlier in B. G. Farley and W. A. Clark,
“Simulation of Self-Organizing Systems by Digital Computer, IRE Transactions on
Information Theory, Vol. 4, pp. 76–84, 1954. (In 1962 Clark built the first personal
computer, the LINC.) [74]
3. Alan Wilkes and Nicholas Wade credit Scottish psychologist Alexander Bain
(1818–1903) with the invention of the first neural network, which Bain described in his 1873
book Mind and Body: The Theories of Their Relation.” (See Alan L. Wilkes and Nicholas J.
Wade, “Bain on Neural Networks,” Brain and Cognition, Vol. 33, pp. 295–305, 1997.) [74]
4. Gerald P. Dinneen, “Programming Pattern Recognition,” Proceedings of the 1955
Western Joint Computer Conference, Institute of Radio Engineers, New York, pp. 94–100,
1955. [74]
5. Oliver Selfridge, “Pattern Recognition and Modern Computers,” Proceedings of the 1955
Western Joint Computer Conference, Institute of Radio Engineers, New York, pp. 91–93,
1955. [75]
6. Allen Newell, “The Chess Machine: An Example of Dealing with a Complex Task by
Adaptation,” Proceedings of the 1955 Western Joint Computer Conference, Institute of
Radio Engineers, New York, pp. 101–108, 1955. (Also issued as RAND Technical Report
P-620.) [75]
7. National Academy of Sciences, Biographical Memoirs, Vol. 71, 1997. Available online at
http://www.nap.edu/catalog.php?record id=5737. [76]
8. Allen Newell, J. C. Shaw, and Herbert A. Simon, “Chess-Playing Programs and the
Problem of Complexity,” IBM Journal of Research and Development, Vol. 2, pp. 320–335,
1958. The paper is available online at http://domino.watson.ibm.com/tchjr/journalindex.
nsf/0/237cfeded3be103585256bfa00683d4d?OpenDocument. [76]
9. From John McCarthy’s informal comments at the 2006 Dartmouth celebration. [77]
10. Nathan Rochester et al., “Tests on a Cell Assembly Theory of the Action of the Brain
Using a Large Digital Computer,” IRE Transaction of Information Theory, Vol. IT-2, pp.
80-93, 1956. [77]
11. From http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html.
Portions of the proposal have been reprinted in John McCarthy, Marvin L. Minsky,
Nathaniel Rochester, and Claude E. Shannon, “A Proposal for the Dartmouth Summer
Research Project on Artificial Intelligence,” AI Magazine, Vol. 27, No. 4, p. 12, Winter
2006. [77]
12. From http://www-formal.stanford.edu/jmc/reviews/bloomfield/bloomfield.html. [78]
13. Pamela McCorduck, Machines Who Think: A Personal Inquiry into the History and
Prospects of Artificial Intelligence, p. 97, San Francisco: W. H. Freeman and Co., 1979. [79]
14. See Allen Newell, “The First AAAI President’s Message,” AI Magazine, Vol. 26, No. 4,
pp. 24–29, Winter 2005. [79]
15. M. L. Minsky, Theory of Neural-Analog Reinforcement Systems and Its Application to
the Brain-Model Problem, Ph.D. thesis, Princeton University, 1954. [79]
16. Marvin L. Minsky, “Steps Toward Artificial Intelligence,” Proceedings of the IRE, Vol.
49, No. 1, pp. 8–30, January 1961. Also appears in Edward A. Feigenbaum, and Julian
86
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![3.3 NOTES
Feldman (eds.), Computers and Thought, New York: McGraw Hill, 1963. (Available online
at http://web.media.mit.edu/∼minsky/papers/steps.html.) [79]
17. Pamela McCorduck, op. cit., p. 116. [80]
18. Herbert A. Simon, Models of My Life, Cambridge, MA: MIT Press, 1996. The quote is
from http://www.post-gazette.com/pg/06002/631149.stm. [80]
19. Ibid. [80]
20. http://www.post-gazette.com/downloads/20060102simon notes.pdf contains sketches of
Simon’s simulation of an LT proof. [80]
21. Pamela McCorduck, op. cit., p. 106. [80]
22. D. V. Blake and A. M. Uttley (eds.), Proceedings of the Symposium on Mechanisation
of Thought Processes, Vols. 1 and 2, London: Her Majesty’s Stationary Office, 1959. [82]
23. John McCarthy, “Artificial Intelligence, Logic and Formalizing Common Sense,” in
Philosophical Logic and Artificial Intelligence, Richmond Thomason (ed.), Dordrecht:
Kluwer Academic, 1989. [82]
24. J. McCarthy, “Situations, Actions and Causal Laws, Stanford Artificial Intelligence
Project,” Memo 2, 1963. The two pieces are reprinted together in M. Minsky (ed.),
Semantic Information Processing, pp. 410–417, Cambridge, MA: MIT Press, 1968. Related
topics are explored in J. McCarthy and Patrick Hayes, “Some Philosophical Ideas From the
Standpoint of Artificial Intelligence,” MI-4, 1969. [83]
25. Oliver G. Selfridge, “Pandemonium: A Paradigm for Learning,” in D. V. Blake and A.
M. Uttley (eds.), Proceedings of the Symposium on Mechanisation of Thought Processes, pp,
511–529, London: Her Majesty’s Stationary Office, 1959. [83]
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![4 Pattern Recognition
hierarchies, with the lowest level at the bottom and the highest at the top. To
simplify the diagram, weight bulges are not shown.] Although the perceptron
illustrated, with only one output unit, is capable of only two different outputs
(1 or 0), multiple outputs (sets of 1’s and 0’s) could be achieved by arranging
for several output units.
Figure 4.5: A perceptron.
The input layer, shown at the bottom of Fig. 4.5, was typically a
rectangular array of 1’s and 0’s corresponding to cells called “pixels” of a
black-and-white image. One of the applications Rosenblatt was interested in
was, like Selfridge, character recognition.
I’ll use some simple algebra and geometry to show how the neural
elements in perceptron networks can be “trained” to produce desired outputs.
94
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![4.2 Neural Networks
hyperplane that perfectly separated a set of training inputs when such a
hyperplane existed.8
(Other mathematicians, such as Albert B. Novikoff at
SRI, later developed more elegant proofs.9
I give a version of this proof in my
book Learning Machines.10
)
Although some feasibility and design work was done using computer
simulations, Rosenblatt preferred building hardware versions of his
perceptrons. (Simulations were slow on early computers, thus explaining the
interest in building special-purpose perceptron hardware.) The MARK I was
an alpha-perceptron built at the Cornell Aeronautical Laboratory under the
sponsorship of the Information Systems Branch of the Office of Naval
Research and the Rome Air Development Center. It was first publicly
demonstrated on 23 June 1960. The MARK I used volume controls (called
“potentiometers” by electrical engineers) for weights. These had small motors
attached to them for making adjustments to increase or decrease the weight
values.
In 1959, Frank Rosenblatt moved his perceptron work from the Cornell
Aeronautical Laboratory in Buffalo, New York, to Cornell University, where he
became a professor of psychology. Together with Block and several students,
Rosenblatt continued experimental and theoretical work on perceptrons. His
book Principles of Neurodynamics provides a detailed treatment of his
theoretical ideas and experimental results.11
Rosenblatt’s last system, called
Tobermory, was built as a speech-recognition device.12
[Tobermory was the
name of a cat that learned to speak in The Chronicles of Clovis, a group of
short stories by Saki (H. H. Munro).] Several Ph.D. students, including George
Nagy, Carl Kessler, R. D. Joseph, and others, completed perceptron projects
under Rosenblatt at Cornell.
In his last years at Cornell, Rosenblatt moved on to study chemical
memory transfer in flatworms and other animals – a topic quite removed from
his perceptron work. Tragically, Rosenblatt perished in a sailing accident in
Chesapeake Bay in 1969.
Around the same time as Rosenblatt’s alpha-perceptron, Woodrow W.
(Woody) Bledsoe (1921–1995) and Iben Browning (1918–1991), two
mathematicians at Sandia Laboratories in Albuquerque, New Mexico, were
also pursuing research on character recognition that used random samplings of
input images. They experimented with a system that projected images of
alphanumeric characters on a 10 × 15 mosaic of photocells and sampled the
states of 75 randomly chosen pairs of photocells. Pointing out that the idea
could be extended to sampling larger groups of pixels, say N of them, they
called their method the “N-tuple” method. They used the results of this
sampling to make a decision about the category of an input letter.13
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![4.4 Applications of Pattern Recognition to Aerial Reconnaissance
currents exceeded a threshold value. Those binary feature decisions
were then summed, again in an analog electrical circuit, not in a
computer, and again a decision [tank or no tank] was made
depending on whether the sum exceeded a threshold value.
In another system, the statistical classification was implemented by a
program, called MULTINORM, running on the Philco 2000 computer.29
In
other experiments Philco used additional statistical tests to weight some of the
feature blocks more heavily than others in computing the final score. Kanal
told me that these experiments with weighting the outputs of the feature
blocks “anticipated the support vector machine (SVM) classification
idea. . . [by] using the first layer to identify the training samples close to the
boundary between tanks and non-tanks.”30
(I’ll describe the important SVM
method in a later chapter.)
Of course, these systems had a rather easy task. All of the tanks were in
standard position and were already isolated in the photograph. (The authors
mention, however, how the system could be adapted to deal with tanks
occurring in any position or orientation in the image.) The photograph in Fig.
4.13 shows a typical tank image. (The nontank images are similar, except
without the tank.)
I find the system interesting not only because of its performance but also
because it is a layered system (similar to Pandemonium and to the
alpha-perceptron) and because it is an example in which the original image is
divided into overlapping subimages, each of which is independently processed.
As I’ll mention later, overlapping subimages play a prominent role in some
computational models of the neocortex.
Unfortunately, the Philco reports giving details of this work aren’t readily
available.31
Furthermore, Philco and some of the other groups engaged in this
work have disappeared. Here is what Tom Harley wrote me about the Philco
reports and about Philco itself:32
Most of the pattern recognition work done at Philco in the 1960s
was sponsored by the DoD [Department of Defense], and the
reports were not available for public distribution. Since then, the
company itself has really vanished into thin air. Philco was bought
by Ford Motor Company in 1961, and by 1966, they had
eliminated the Philco research labs where Laveen [Kanal] and I
were working. Ford tried to move our small pattern recognition
group to Newport Beach, CA [the location of Ford’s Aeronutronic
Division, whose pattern recognition group folded later also], and
when we all decided not to go, they transferred us to their
Communications Division, and told us to close out our pattern
recognition projects. Laveen eventually went off to the University
of Maryland, and in 1975, I transferred to the Ford Aerospace
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![4.4 NOTES
Transformation Committee) organized the first “Pattern Recognition
Workshop,” which was held in Puerto Rico in October 1966.33
A second one
(which I attended) was held in Delft, The Netherlands, in August 1968. In
1966, this subgroup became the IEEE Computer Society Pattern Analysis and
Machine Intelligence (PAMI) Technical Committee, which continued to
organize conferences and workshops.34
Meanwhile, during the late 1950s and early 1960s, the symbol-processing
people did their work mainly at MIT, at Carnegie Mellon University, at IBM,
and at Stanford University. I’ll turn next to describing some of what they did.
Notes
1. See http://www.nist.gov/public affairs/techbeat/tb2007 0524.htm. [89]
2. Russell A. Kirsch et al., “Experiments in Processing Pictorial Information with a Digital
Computer,” Proceedings of the Eastern Joint Computer Conference, pp. 221–229, Institute
of Radio Engineering and Association for Computing Machinery, December 1957. [89]
3. The proceedings of the conference were published in George L. Fischer Jr. et al., Optical
Character Recognition, Washington, DC: Spartan Books, 1962. [90]
4. From J. Rabinow, “Developments in Character Recognition Machines at Rabinow
Engineering Company,” in George L. Fischer Jr. et al., op. cit., p. 27. [90]
5. From http://www.sri.com/about/timeline/erma-micr.html. [90]
6. Oliver G. Selfridge and Ulrich Neisser, “Pattern Recognition by Machine,” Scientific
American, Vol. 203, pp. 60–68, 1960. (Reprinted in Edward A. Feigenbaum and Julian
Feldman (eds.), Computers and Thought, pp. 237ff, New York: McGraw Hill, 1963.) [91]
7. An early reference is Frank Rosenblatt, “The Perceptron: A Probabilistic Model for
Information Storage and Organization in the Brain,” Psychological Review, Vol. 65, pp.
386ff, 1958. [92]
8. H. David Block, “The Perceptron: A Model for Brain Functioning,” Reviews of Modern
Physics, Vol. 34, No. 1, pp. 123–135, January 1962. [97]
9. Albert B. J. Novikoff, “On Convergence Proofs for Perceptrons,” in Proceedings of the
Symposium on Mathematical Theory of Automata, pp. 615–622, Brooklyn, NY: Polytechnic
Press of Polytechnic Inst. of Brooklyn, 1963. [97]
10. Nils J. Nilsson, Learning Machines: Foundations of Trainable Pattern-Classifying
Systems, New York: McGraw-Hill Book Co., 1965; republished as The Mathematical
Foundations of Learning Machines, San Francisco: Morgan Kaufmann Publishers, 1990. [97]
11. Frank Rosenblatt, Principles of Neurodynamics, Washington, DC: Spartan Books, 1962.
[97]
12. Frank Rosenblatt, “A Description of the Tobermory Perceptron,” Collected Technical
Papers, Vol. 2, Cognitive Systems Research Program, Cornell University, 1963. [97]
13. Woodrow W. Bledsoe and Iben Browning, “Pattern Recognition and Reading by
Machine,” Proceedings of the Eastern Joint Computer Conference, pp. 225–232, New York:
Association for Computing Machinery, 1959. [97]
14. William C. Ridgway, “An Adaptive Logic System with Generalizing Properties,”
Stanford Electronics Laboratories Technical Report 1556-1, Stanford University, Stanford,
CA, 1962. [98]
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![4 NOTES
15. For a description of MINOS II, see Alfred E. Brain, George Forsen, David Hall, and
Charles Rosen, “A Large, Self-Contained Learning Machine,” Proceedings of the Western
Electronic Show and Convention, 1963. The paper was reprinted as Appendix C of an SRI
proposal and is available online at
http://www.ai.sri.com/pubs/files/rosen65-esu65-1tech.pdf. [100]
16. For a discussion of shift-register codes and other codes, see W. Peterson,
Error-Correcting Codes, New York: John Wiley & Sons, 1961. Our technique was reported
in A. E. Brain and N. J. Nilsson, “Graphical Data Processing Research Study and
Experimental Investigation,” Quarterly Progress Report No. 8, p. 11, SRI Report, June
1962; available online at http://www.ai.sri.com/pubs/files/1329.pdf. [100]
17. Robert E. Larsen of SRI suggested using this method. The online encyclopedia
Wikipedia has a clear description of dynamic programming. See
http://en.wikipedia.org/wiki/Dynamic programming.
[101]
18. The technical details of the complete system are described in two papers: John Munson,
“Experiments in the Recognition of Hand-Printed Text: Part I – Character Recognition,”
and Richard O. Duda and Peter E. Hart, “Experiments in the Recognition of Hand-Printed
Text: Part II – Context Analysis,” AFIPS Conference Proceedings, (of the 1968 Fall Joint
Computer Conference), Vol. 33, pp. 1125–1149, Washington, DC: Thompson Book Co.,
1968. Additional information can be found in SRI AI Center Technical reports, available
online at http://www.ai.sri.com/pubs/files/1343.pdf and
http://www.ai.sri.com/pubs/files/1344.pdf. [102]
19. E. Fix and J. L. Hodges Jr., “Discriminatory analysis, nonparametric discrimination,”
USAF School of Aviation Medicine, Randolph Field, Texas, Project 21-49-004, Report 4,
Contract AF41(128)-31, February 1951. See also B. V. Dasarathy (ed.), Nearest Neighbor
(NN) Norms: NN Pattern Classification Techniques, Los Alamitos, CA: IEEE Computer
Society Press, which is a reprint of 1951 unpublished work of Fix and Hodges. [103]
20. Thomas M. Cover and Peter E. Hart, “Nearest Neighbor Pattern Classification,” IEEE
Transactions on Information Theory, pp. 21–27, January 1967. Available online at
http://ieeexplore.ieee.org/iel5/18/22633/01053964.pdf. [104]
21. See M. A. Aizerman, E. M. Braverman, and L. I. Rozonoer, “Theoretical Foundations of
the Potential Function Method in Pattern Recognition Learning,” Automation and Remote
Control, Vol. 25, pp. 917–936, 1964, and A. G. Arkadev and E. M. Braverman, Computers
and Pattern Recognition, (translated from the Russian by W. Turski and J. D. Cowan),
Washington, DC: Thompson Book Co., Inc., 1967. [104]
22. George S. Sebestyen, Decision-Making Processes in Pattern Recognition, Indianapolis,
IN: Macmillan Publishing Co., Inc., 1962. [104]
23. Nils J. Nilsson, op. cit. [104]
24. Richard O. Duda and Peter E. Hart, Pattern Classification and Scene Analysis, New
York: John Wiley & Sons, 1973; updated version: Richard O. Duda, Peter E. Hart, and
David G. Stork, Pattern Classification, 2nd Edition, New York: John Wiley & Sons, 2000.
[104]
25. George Nagy, “State of the Art in Pattern Recognition,” Proceedings of the IEEE, Vol.
56, No. 5, pp. 836–857, May 1968. [104]
26. See, for example, Joseph K. Hawkins and C. J. Munsey, “An Adaptive System with
Direct Optical Input,” Proceedings of the IEEE, Vol. 55, No. 6, pp. 1084–1085, June 1967.
Available online for IEEE members at http://ieeexplore.ieee.org/iel5/5/31078/01446273.
pdf?tp=&arnumber=1446273&isnumber=31078. [105]
27. Laveen N. Kanal and Neal C. Randall, “Target Detection in Aerial Photography,” paper
8.3, Proceedings of the 1964 Western Electronics Show and Convention (WESCON), Los
Angeles, CA, Institute of Radio Engineers (now IEEE), August 25–28, 1964. (Several other
110
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![4.4 NOTES
papers on pattern recognition were presented at this conference and are contained in the
proceedings.) [105]
28. Thomas Harley, personal e-mail communication, July 15, 2007. [106]
29. Laveen N. Kanal and Neal C. Randall, op. cit.. [107]
30. Laveen Kanal, personal e-mail communication, July 13, 2007. [107]
31. Laveen N. Kanal, “Statistical Methods for Pattern Classification,” Philco Report, 1963;
originally appeared in T. Harley et al., “Semi-Automatic Imagery Screening Research Study
and Experimental Investigation,” Philco Reports VO43-2 and VO43-3, Vol. I, Sec. 6, and
Appendix H, prepared for U.S. Army Electronics Research and Development Laboratory
under Contract DA-36-039-SC- 90742, March 29, 1963. [107]
32. Thomas Harley, personal e-mail communication, July 11, 2007. [107]
33. Laveen N. Kanal (ed.), Pattern Recognition, Proceedings of the IEEE Workshop on
Pattern Recognition, held at Dorado, Puerto Rico, Washington, DC: Thompson Book Co.,
1968. [109]
34. See the Web page at http://tab.computer.org/pamitc/. [109]
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![5 Early Heuristic Programs
and three missionaries must cross a river. Their boat can only hold two
people. If the cannibals outnumber the missionaries, on either side of the river,
the missionaries on that side perish. Each missionary and each cannibal can
row the boat. How can all six get across the river safely? Most people have no
trouble formulating this puzzle as a search problem, and the solution is
relatively easy. But it does require making one rather nonintuitive step. The
computer scientist and AI researcher Saul Amarel (1928–2002) wrote a
much-referenced paper analyzing this puzzle and various extended versions of
it in which there can be various numbers of missionaries and cannibals.7
(The
extended versions don’t appear to be so easy.) After moving from one
representation to another, Amarel finally developed a representation for a
generalized version of the problem whose solution required virtually no search.
AI researchers are still studying how best to represent problems and, most
importantly, how to get AI systems to come up with their own representations.
5.2 Proving Theorems in Geometry
Nathan Rochester returned to IBM after the Dartmouth workshop excited
about discussions he had had with Marvin Minsky about Minsky’s ideas for a
possible computer program for proving theorems in geometry. He described
these ideas to a new IBM employee, Herb Gelernter (1929– ). Gelernter soon
began a research project to develop a geometry-theorem-proving machine. He
presented a paper on the first version of his program at a conference in Paris
in June 1959,8
acknowledging that
[t]he research project itself is a consequence of the Dartmouth
Summer Research Project on Artificial Intelligence held in 1956,
during which M. L. Minsky pointed out the potential utility of the
diagram to a geometry theorem-proving machine.
Gelernter’s program exploited two important ideas. One was the explicit
use of subgoals (sometimes called “reasoning backward” or “divide and
conquer”), and the other was the use of a diagram to close off futile search
paths.
The strategy taught in high school for proving a theorem in geometry
involves finding some subsidiary geometric facts from which, if true, the
theorem would follow immediately. For example, to prove that two angles are
equal, it suffices to show that they are corresponding angles of two
“congruent” triangles. (A triangle is congruent to another if it can be
translated and rotated, possibly even flipped over, in such a way that it
matches the other exactly.) So now, the original problem is transformed into
the problem of showing that two triangles are congruent. One way (among
others) to show that two triangles are congruent is to show that two
corresponding sides and the enclosed angle of the two triangles all have the
118
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![5 Early Heuristic Programs
In 1956 I wrote two memos about a hand-simulated program for
proving theorems in geometry. In the first memo, the procedure
found the simple proof that if a triangle has two equal sides then
the corresponding angles are equal. It did this by noticing that
triangle ABC was congruent to triangle CBA because of
“side-angle-side.” What was interesting is that this was found after
a very short search – because, after all, there weren’t many things
to do. You might say the program was too stupid to do what a
person might do, that is, think, “Oh, those are both the same
triangle. Surely no good could come from giving it two different
names.” (The program has a collection of heuristic methods for
proving Euclid-Like theorems, and one was that “if you want to
prove two angles are equal, show that they’re corresponding parts
of congruent triangles.” Then it also had several ways to
demonstrate congruence. There wasn’t much more in that first
simulation.) But I can’t find that memo anywhere.
As Minsky said, this is a very easy problem for a computer. Gelernter’s
program proved much more difficult theorems, and for these his use of a
diagram was essential. The program did not literally draw and look at a
diagram. Instead, as Gelernter wrote,
[The program is] supplied with the diagram in the form of a list of
possible coordinates for the points named in the theorem. This
point list is accompanied by another list specifying the points
joined by segments. Coordinates are chosen to reflect the greatest
possible generality in the figures.
So, for example, the points named in the problem about proving two
angles equal are the vertices of the triangle ABC, namely, points A and B and
C. Coordinates for each of these points are chosen, and care is taken to make
sure that these coordinates do not happen to satisfy any special unnamed
properties.
Gelernter’s program worked by setting up subgoals and subsubgoals such
as those I used in the example just given. It then searched for a chain of these
ending in subgoals that could be established directly from the premises.
Before any subgoal was selected by the program to be worked on however, it
was first tested to see whether it held in the diagram. If it did hold, it might
possibly be provable and could therefore be considered as a possible route to a
proof. But, if it did not hold in the diagram, it could not possibly be true.
Thus, it could be eliminated from further consideration, thereby “pruning” the
search tree and saving what would certainly be fruitless effort. Later work in
AI would also exploit “semantic” information of this sort.
We can see similarities between the strategies used in the geometry
program and those used by humans when we solve problems. It is common for
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![5 Early Heuristic Programs
More dramatic early success, however, was achieved on the simpler game
of checkers (or draughts as the game is known in British English). Arthur
Samuel (Fig. 5.5) began thinking about programming a computer to play
checkers in the late 1940s at the University of Illinois where he was a Professor
of Electrical Engineering. In 1949, he joined IBM’s Poughkeepsie Laboratory
and completed his first operating checkers program in 1952 on IBM’s 701
computer. The program was recoded for the IBM 704 in 1954. According to
John McCarthy,20
“Thomas J. Watson Sr., the founder and President of IBM,
remarked that the demonstration [of Samuel’s program] would raise the price
of IBM stock 15 points. It did.”
[Apparently, Samuel was not the first to write a checkers-playing program.
According to the Encyclopedia Brittanica, Online, “The earliest successful AI
program was written in 1951 by Christopher Strachey, later director of the
Programming Research Group at the University of Oxford. Strachey’s
checkers (draughts) program ran on the Ferranti Mark I computer at the
University of Manchester, England. By the summer of 1952 this program
could play a complete game of checkers at a reasonable speed.”]21
Samuel’s main interest in programming a computer to play checkers was
to explore how to get a computer to learn. Recognizing the “time consuming
and costly procedure[s]” involved in programming, Samuel wrote
“Programming computers to learn from experience should eventually eliminate
the need for much of this detailed programming effort.”22
Samuel’s efforts
were among the first in what was to become a very important part of artificial
intelligence, namely, “machine learning.” His first program that incorporated
learning was completed in 1955 and demonstrated on television on February
24, 1956.
Before describing his learning methods, I’ll describe in general how
Samuel’s program chose moves. The technique is quite similar to how moves
were chosen in the eight-puzzle I described earlier. Except now, provision must
be made for the fact that the opponent chooses moves also. Again, a tree of
symbolic expressions, representing board positions, is constructed. Starting
with the initial configuration, all possible moves by the program (under the
assumption that the program moves first) are considered. The result is all the
possible resulting board configurations branching out from the starting
configuration. Then, from each of these, all possible moves of the opponent are
considered – resulting in more branches, and so on.
If such a tree could be constructed for an entire game, a winning move
could be computed by examination of the tree. Unfortunately, it has been
estimated that there are about 5 × 1020
possible checkers positions. A leading
expert in programming computers to play games, Jonathan Schaeffer, was able
to “solve” checkers (showing that optimal play by both players results in a
draw) by time-consuming analysis of around 1014
positions. He wrote me that
“This was the result of numerous enhancements aimed at focussing the search
at the parts of the search space where we were most likely to find what we
124
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![5.4 Game-Playing Programs
(The program had access to 38 such features but only used the 16 best of these
at any one time.) The points contributed by each feature were then multiplied
by a “weight” (reflecting the relative importance of its corresponding feature),
and the result was summed to give an overall score for a position.
Starting with a position immediately above those at the tip of the tree, if
it is a position for which it is the program’s turn to move, we can assume that
the program would want to move to that position with the highest score, so
that highest score is migrated back to this “immediately above” position. If,
however, it is a position from which it is the opponent’s turn to move, we
assume that the opponent would want to move to that position with the lowest
score. In that case, the lowest score is migrated back to this immediately
above position. This alternately “highest–lowest” migration strategy is
continued back all the way up the tree and is called the “minimax” strategy.
[A simple modification of this strategy, called the “alpha–beta” procedure,
is used to infer (correctly) from already-migrated scores that certain branches
need not be examined at all – thus allowing other branches to be explored
more deeply. Opinions differ about who first thought of this important
modification. McCarthy and Newell and Simon all claim credit. Samuel told
me he used it but that it was too obvious to write about.]
If one assumes that it is the program’s turn to move from the current
position, and that scores have already been migrated back to the positions just
below it, the program would make its move to that position with the highest
score. And then the game would continue with the opponent making a move,
another stage of tree growth, score computation and migration, and so on
until one side wins or loses.
One of the learning methods in Samuel’s program adjusted the values of
the weights used by the scoring system. (Recall that weight adjustments in
Pandemonium and in neural networks were ways in which those systems
learned.) The weights were adjusted so that the score of a board position (as
computed by the sum of the weighted feature scores) moved closer to the value
of its migrated score after finishing a search. For example, if the score of an
initial position was computed (using the weights before adjustment) to be 22,
and the migrated score of that position after search was 30, then the weights
used to compute the score of the initial position were adjusted in a manner so
that the new score (using the adjusted value of the weights) was made closer
to 30, say 27. (This technique foreshadowed a very important learning method
later articulated by Richard Sutton called “temporal-difference learning.”) The
idea here was that the migrated score, depending as it did on looking ahead in
the game, was presumed to be a better estimate than the original score. The
estimating procedure was thereby improved so that it produced values more
consistent with the “look-ahead” score.
Samuel also used another method called “rote learning” in which the
program saved various board positions and their migrated scores encountered
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![5 NOTES
during actual play. Then, at the end of a search, if a leaf position encountered
was the same as one of these stored positions, its score was already known
(and would not have to be computed using the weights and features.) The
known score, based as it was on a previous search, would presumably be a
better indicator of position value than would be the computed score.
Samuel’s program also benefitted from the use of “book games,” which
are records of the games of master checkers players. In commenting about
Samuel’s work, John McCarthy wrote that “checker players have many
volumes of annotated games with the good moves distinguished from the bad
ones. Samuel’s learning program used Lee’s Guide to Checkers24
to adjust its
criteria for choosing moves so that the program would choose those thought
good by checker experts as often as possible.”
Samuel’s program played very good checkers and, in the summer of 1962,
beat Robert Nealey, a blind checkers master from Connecticut. (You can see a
game played between Mr. Nealey and Samuel’s program at
http://www.fierz.ch/samuel.htm.) But, according to Jonathan Schaeffer and
Robert Lake, “In 1965, the program played four games each against Walter
Hellman and Derek Oldbury (then playing a match for the World
Championship), and lost all eight games.”25
Notes
1. A. Newell and H. A. Simon, “The Logic Theory Machine: A Complex Information
Processing System,” Proceedings IRE Transactions on Information Theory, Vol. IT-2, pp.
61–79, September 1956, and A. Newell, J. C. Shaw, and H. A. Simon, “Empirical
Explorations of the Logic Theory Machine: A Case Study in Heuristics,” Proceedings of the
1957 Western Joint Computer Conference, Institute of Radio Engineers, pp. 218–230, 1957.
[113]
2. Alfred North Whitehead and Bertrand Russell, Principia Mathematica, Vol. 1,
Cambridge: Cambridge University Press, 1910. [113]
3. Allen Newell and Herbert A. Simon, “Computer Science as Empirical Inquiry: Symbols
and Search,” Communications of the ACM, Vol. 19, No. 3, pp. 113–126, March 1976. [114]
4. Claude E. Shannon, “Programming a Computer for Playing Chess,” Philosophical
Magazine, Ser. 7, Vol. 41, No. 314, March 1950. Text available online at
http://www.pi.infn.it/∼carosi/chess/shannon.txt. (The paper was first presented in March
1950 at the National Institute for Radio Engineers Convention in New York.) [117]
5. Pamela McCorduck, Machines Who Think: A Personal Inquiry into the History and
Prospects of Artificial Intelligence, p. 106, San Francisco: W. H. Freeman and Co., 1979.
[117]
6. John McCarthy, “A Tough Nut for Theorem Provers,” Stanford Artificial Intelligence
Project Memo No. 16, July 17, 1964; available online at
http://www-formal.stanford.edu/jmc/toughnut.pdf. [117]
7. Saul Amarel, “On Representations of Problems of Reasoning About Actions,” in Donald
Michie (ed.), Machine Intelligence 3, pp. 131–171, Edinburgh: Edinburgh University Press,
1968. [118]
128
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![5.4 NOTES
8. Herbert Gelernter, “Realization of a Geometry-Theorem Proving Machine,” Proceedings
of the International Conference on Information Processing”, pp. 273–282, Paris: UNESCO
House, Munich: R. Oldenbourg, and London: Butterworths, 1960. Also in Edward A.
Feigenbaum and Julian Feldman (eds.), Computers and Thought, pp. 134–152, New York:
McGraw Hill, 1963. [118]
9. From http://www.math.niu.edu/∼rusin/known-math/99/minsky. [119]
10. Allen Newell, J. C. Shaw, and Herbert A. Simon, “Report on a General Problem-Solving
Program,” Proceedings of the International Conference on Information Processing”, pp.
256–264, Paris: UNESCO House, Munich: R. Oldenbourg, and London: Butterworths, 1960.
[121]
11. For more about GPS as a theory and explanation for human problem solving, see Allen
Newell and Herbert Simon, “GPS, a Program That Simulates Human Thought,” in H.
Billings (ed.), Lernende Automaten, pp. 109–124, Munich: R. Oldenbourg KG, 1961.
Reprinted in Computers and Thought, pp. 279–293. [121]
12. I adapt an example from
http://www.math.grinnell.edu/∼stone/events/scheme-workshop/gps.html. [121]
13. See George Ernst and Allen Newell, GPS: A Case Study in Generality and Problem
Solving, New York: Academic Press, 1969. [122]
14. James R. Slagle, “A Heuristic Program That Solves Symbolic Integration Problems in
Freshman Calculus,” Ph.D. dissertation, MIT, May 1961. For an article about SAINT, see
James R. Slagle, “A Heuristic Program That Solves Symbolic Integration Problems in
Freshman Calculus,” Journal of the ACM, Vol. 10, No. 4, pp. 507–520, October 1963. [123]
15. Allen Newell, J. Shaw, and Herbert Simon, “Chess-Playing Programs and the Problem
of Complexity,” IBM Journal of Research and Development, Vol. 2, pp. 320–335, October
1958. [123]
16. Chapter 5 of Hal’s Legacy: 2001’s Computer as Dream and Reality, David G. Stork
(ed.), Cambridge, MA: MIT Press, 1996. See the Web site at
http://mitpress.mit.edu/e-books/Hal/chap5/five3.html. [123]
17. Andrew Hodges, “Alan Turing and the Turing Test,” in Parsing the Turing Test:
Philosophical and Methodological Issues in the Quest for the Thinking Computer, Robert
Epstein, Gary Roberts, and Grace Beber (ed.), Dordrecht, The Netherlands: Kluwer, 2009.
See A. M. Turing, “Proposed Electronic Calculator,” report for National Physical
Laboratory, 1946, in A. M. Turing’s ACE Report of 1946 and Other Papers, B. E.
Carpenter and R. W. Doran (eds.), Cambridge, MA: MIT Press, 1986. [123]
18. http://en.wikipedia.org/wiki/Alan Turing. [123]
19. Marvin Minsky (ed.), “Introduction,” Semantic Information Processing, p. 12,
Cambridge, MA: MIT Press, 1968. [123]
20. From a Web retrospective at
http://www-db.stanford.edu/pub/voy/museum/samuel.html. [124]
21. See Christopher Strachey, “Logical or Non-mathematical Programmes,” Proceedings of
the 1952 ACM National Meeting (Toronto), pp. 46–49, 1952. [124]
22. Arthur L. Samuel, “Some Studies in Machine Learning Using the Game of Checkers,”
IBM Journal of Research and Development, Vol. 3, No. 3, pp. 210–229, 1959. Reprinted in
Edward A. Feigenbaum and Julian Feldman (eds.), Computers and Thought, p. 71, New
York: McGraw Hill, 1963. [124]
23. E-mail of February 14, 2009. [125]
24. John W. Dawson, Lee’s Guide to the Game of Draughts, Revised Edition, London: E.
Marlborough, 1947. [128]
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![5 NOTES
25. Jonathan Schaeffer and Robert Lake, “Solving the Game of Checkers,” Games of No
Chance, pp. 119–133, MSRI Publications, Vol. 29, 1996. (Available online at
http://www.msri.org/communications/books/Book29/files/schaeffer.pdf.) [128]
130
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![6.1
Chapter 6
Semantic Representations
The computer programs I have described so far performed transformations on
relatively simple symbol structures, which were all that were required for the
mathematical problems, puzzles, and games that these programs dealt with.
The main effort was in coming up with and using problem-specific heuristics
(such as features to be used in computing the value of a checkers position, for
example) to limit the number of transformations of these structures in
searches for solutions. As Minsky put it, “The most central idea of the
pre-1962 period was that of finding heuristic devices to control the breadth of
a trial-and-error search.”1
In the early 1960s, several Ph.D. research projects,
some performed under Minsky’s direction at MIT, began to employ more
complex symbol structures in programs for performing various intellectual
tasks. Because of their rich, articulated content of information about their
problem topic, these structures were called semantic representations.2
As
Minsky wrote, “Within the small domain in which each program operates, the
performance [of these programs] is not too bad compared with some human
activities. . . . But much more important than what these particular
experiments achieve are the methods they use to achieve what they do, for
each is a first trial of previously untested ideas.”3
I’ll describe some examples
of these sorts of projects and the new methods that they employed.
6.1 Solving Geometric Analogy Problems
Thomas G. Evans (1934– ) programmed a system that was able to perform
well on some standard geometric analogy tests. It was apparently the largest
program written up to that time in John McCarthy’s new programming
language, LISP (which I’ll describe later). I quote from an article based on
Evans’s 1963 dissertation, which presented this work:4
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![6 Semantic Representations
We shall be considering the solution by machine of so-called
“geometric-analogy” intelligence-test questions. Each member of
this class of problems consists of a set of labeled line drawings. The
task to be performed can be described by the question: “Figure A
is to Figure B as Figure C is to which of the following figures?” For
example [in Fig. 6.1] it seems safe to say that most people would
agree with the program we are about to describe, in choosing
[number 4] as the desired answer.
Figure 6.1: An analogy problem.
He further noted that “problems of this type are widely regarded as
requiring a high degree of intelligence for their solution and in fact are used as
a touchstone of intelligence in some general intelligence tests used for college
admission and other purposes.” So, again, AI research concentrated on
mechanizing tasks requiring human intelligence.
Evans’s program first transformed the diagrams presented to it so that
they revealed how they were composed out of parts. He called these
“articular” representations. Of the possibly several decompositions possible,
the one chosen by the program depended on its “context.” (This choice is one
example of a heuristic used by the program.) For example, the diagram
could either be decomposed into
132
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![6.1 Solving Geometric Analogy Problems
and
or into
and
But if the analogy problem contained another diagram (part of the context):
then the first decomposition would be chosen.
Evans represented diagrams and their parts as complex symbol structures
consisting of rather elaborate combinations of lists and lists of lists whose
elements indicated which parts were inside or outside (or above or below)
which other parts, and so on. Those details need not concern us here, but they
did allow Evans to specify “rules” for his program that could be used to show
how one diagram could be transformed into another. The program was able to
infer which combinations of these rules transformed Figure A of a given
problem into Figure B. Then it could apply this transformation to Figure C. If
one of the multiple-choice answers resulted, it would give that one as its
answer. Otherwise, the program “weakened” the transformation just enough
so that one of the answers was produced, and that would be the program’s
answer.
Evans summarized his results as follows:
Allowing ourselves only [the parts of the program actually
implemented], our estimate would be that of the 30
geometric-analogy problems on a typical edition of the ACE tests,
[the program] can successfully solve at least 15 and possibly as
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![6 Semantic Representations
many as 20 problems.
He notes that this level of performance compares favorably with the
average high school student.
6.2 Storing Information and Answering
Questions
Another of Minsky’s Ph.D. students during the early 1960s, Bertram Raphael
(1936– ), focused on the problem of “machine understanding.” In his
dissertation,5
Raphael explained that
a computer should be considered able to “understand” if it can
converse intelligently, i.e., if it can remember what it is told,
answer questions, and make responses which a human observer
considers reasonable.
Raphael wanted to be able to tell things to a computer and then ask it
questions whose answers could be deduced from the things it had been told.
(The telling and asking were to be accomplished by typing sentences and
queries.) Here are some examples of the kinds of things he wanted to tell it:
Every boy is a person.
A finger is part of a hand.
There are two hands on each person.
John is a boy.
Every hand has five fingers.
Given this information, Raphael would want his system to be able to
deduce the answer to the question “How many fingers does John have?”
Because Raphael wanted his system to communicate with people, he
wanted its input and output languages to be “reasonably close to natural
English.” He recognized that “the linguistic problem of transforming natural
language input into a usable form will have to be solved before we obtain a
general semantic information retrieval system.” This “linguistic problem” is
quite difficult and still not “solved” even though much progress has been made
since the 1960s. Raphael used various “devices” (as he called them and which
are not germane to our present discussion) to “bypass [the general problem of
dealing with natural language] while still utilizing understandable English-like
input and output.”
134
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![6 Semantic Representations
to the property-list [that is, links] of the individual concerned before
looking at the descriptions of sets to which the individual belongs.
The justification for this departure from the no-exception
principles of Aristotelian logic is that this precedence of specific
facts over background knowledge seems to be the way people
operate, and I wish the computer to communicate with people as
naturally as possible.
The present program does not experience the uncomfortable feeling
people frequently get when they must face facts like “a whale is a
mammal which lives in water although mammals as a rule live on
land.”
The exception principle was studied by AI researchers in much more
detail later and led to what is called default reasoning and nonmonotonic
logics, as we shall see.
6.3 Semantic Networks
It is instructive to think of SIR’s representational scheme in terms of a
network. The entities (such as JOHN and BOY) are the “nodes” of the network,
and the relational links (such as SUB-SET) are the connections between nodes.
SIR was an early version of what would become an important representational
idea in artificial intelligence, namely, semantic networks. It was not the first,
however. John Sowa, who has written extensively about semantic networks,
claims that the “oldest known semantic network was drawn in the 3rd century
AD by the Greek philosopher Porphyry in his commentary on Aristotle’s
categories.”8
In 1961 Margaret Masterman (1910–1986), Director of the
Cambridge Language Research Unit, used a semantic network in a translation
system in which concepts were ordered in a hierarchy.9
M. Ross Quillian, a student of Herb Simon’s at the Carnegie Institute of
Technology, was interested, along with Newell and Simon, in computational
models of human mental processes, specifically memory organization. He
developed a memory model consisting of a semantic network of nodes
representing English words. The nodes were interconnected by what he called
“associative links.” In Quillian’s words, “In the memory model, ingredients
used to build up a concept are represented by the token nodes naming other
concepts, while the configurational meaning of the concept is represented by
the particular structure of interlinkages connecting those token nodes to each
other.”
Quillian goes on to write that “[t]he central question asked in this
research has been: What constitutes a reasonable view of how semantic
information is organized within a person’s memory? In other words: What
136
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![6 NOTES
To use one of Quillian’s examples, suppose we wanted to compare the
words “cry” and “comfort.” The spreading activations would intersect at the
word “sad,” and the English account would express something like “to cry is
to make a sad sound, and to comfort is to make something less sad.”
Quillian was also interested in how the network could be used to
“disambiguate” two possible uses of the same word. Consider, for example, the
sentence “After the strike, the president sent him away.” The network can
encode different meanings of the word “strike.” One such might involve a labor
dispute, another might involve baseball, and yet another involve a raid by
military aircraft. Which of these meanings is intended by the sentence?
Presumably, activation proceeding outward from the word “president” would
eventually reach concepts having to do with labor disputes before reaching
concepts having to do with baseball or the military. Thus, the “labor dispute”
meaning would be preferred because it is “closer,” given that the word
“president” is in the sentence. In contrast, a different conclusion would be
reached for the sentence “After the strike, the umpire sent him away.”
Quillian’s model differs from some later semantic networks in that it does
not have a predetermined hierarchy of superclasses and subclasses. As Quillian
puts it, “every word is the patriarch of its own separate hierarchy when some
search process starts with it. Similarly, every word lies at various places down
within the hierarchies of (i.e., is an ingredient in) a great many other word
concepts, when processing starts with them.”
Notes
1. Marvin Minsky (ed.), “Introduction,” Semantic Information Processing, p. 9,
Cambridge, MA: MIT Press, 1968. [131]
2. It might be argued that the diagram used by Gelernter’s geometry program was an
earlier use of a semantic representation. [131]
3. Marvin Minsky, op. cit., p. 1. [131]
4. Thomas G. Evans, “A Program for the Solution of a Class of Geometric-Analogy
Intelligence-Test Questions,” in Marvin L. Minsky, op. cit., p. 271. [131]
5. Bertram Raphael, “SIR: Semantic Information Retrieval,” in Marvin Minsky, op. cit.,
pp. 33–145. (This is a partial reprint of his 1964 Ph.D. dissertation.) [134]
6. Marvin Minsky, op. cit., p. 35. [135]
7. Marvin Minsky, op. cit., p. 17. [135]
8. From an article by John F. Sowa at http://www.jfsowa.com/pubs/semnet.htm. (This is
a revised and extended version of one that was originally written for the Encyclopedia of
Artificial Intelligence, edited by Stuart C. Shapiro, Wiley, 1987, second edition, 1992.) [136]
9. Margaret Masterman, “Semantic Message Detection for Machine Translation, Using an
Interlingua,” in Proceedings of the 1961 International Conference on Machine Translation
of Languages and Applied Language Analysis, pp. 438–475, London: Her Majesty’s
Stationery Office, 1962. [136]
138
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![6.3 NOTES
10. M. Ross Quillian, “Semantic Memory,” Ph.D. dissertation, Carnegie Institute of
Technology (now Carnegie Mellon University), October 1966. (This work also appears as
Report AFCRL-66-189 and is partially reprinted in M. Minsky (ed.), Semantic Information
Processing, pp. 216–270, Cambridge, MA: MIT Press, 1968.) [137]
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![7 Natural Language Processing
an important part of natural language processing involves using the grammar
rules to find acceptable parse trees for sentences. Finding a parse tree involves
search – either for the several different ways that the nonterminal symbols,
beginning with “S,” can be replaced using grammar rules in an attempt to
match a target sentence or for the several different ways the words in a target
sentence can be replaced by nonterminal symbols in an attempt to produce the
symbol “S.” The first of these kinds of searches is called “top-down” (from “S”
to a sentence); the second is called “bottom-up” (from a sentence to “S”).
It is often (if not usually) the case that, given a grammar, sentences can
have more than one parse tree, each with a different meaning. For example,
“the man hit the ball in the park” could have a parse tree in which “in the
park” is part of a verb phrase along with “hit” or a parse tree in which “in the
park” is part of a noun phrase along with “ball.” Moreover, as I have already
mentioned, some parsings of sentences might be meaningless. For example,
according to my simple grammar, “the ball threw the man” is a legal but
probably meaningless sentence. Deciding which parse tree is appropriate is
part of the process of deciding on meaning and is a job for the semantics (and
possibly even the pragmatics) level. During the late 1950s and throughout
most of the 1960s and beyond, syntactic analysis was more highly developed
than was semantics.
Semantic analysis usually involves using the parse tree to guide the
transformation of the input sentence into an expression in some well-defined
“meaning representation language” or into a program that responds in the
appropriate way to the input sentence. For example, “the man threw the ball”
might be transformed into a logical expression such as
(∃x, y, z)[Past(z) ∧ Man(x, z) ∧ Ball(y, z) ∧ Event(z) ∧ Throws(x, y, z)],
which can be interpreted as “there are x, y, and z, such that z is an event that
occurred in the past, x is a man in that event, y is a ball in that event, and x
throws y in that event.”
Semantic analysis might also transform the sentence “the man threw the
ball” into a program that, in some way, simulates a man throwing the ball in
the past.
7.2 Machine Translation
Some of the first attempts to use computers for more than the usual numerical
calculations were in automatic translation of sentences in one language into
sentences of another. Word dictionaries could be stored in computer memory
(either on tapes or on punched cards), and these could be used to find English
equivalents for foreign words. It was thought that selecting an appropriate
equivalent for each foreign word in a sentence, together with a modest amount
146
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![7.3 NOTES
Notes
1. Noam Chomsky, Syntactic Structures, ’s-Gravenhage: Mouton & Co., 1957. [142]
2. The basic structure of PSGs was independently invented by computer scientist John
Backus to describe the syntax of the ALGOL programming language. See John Backus,
“The Syntax and Semantics of the Proposed International Algebraic Language of the Zürich
ACM-GAMM Conference,” Proceedings on the International Conference on Information
Processing, pp. 125–132, UNESCO, 1959. [142]
3. According to C. George Boeree (see http://www.ship.edu/∼cgboeree/wundtjames.html),
Wilhelm Wundt “invented the tree diagram of syntax we are all familiar with in linguistics
texts.” [143]
4. For a survey of work during this period, see Daniel Bobrow, “Syntactic Analysis of
English by Computer: A Survey,” Proceedings of the 1963 Fall Joint Computer Conference,
Vol. 24, pp. 365–387, Baltimore: Spartan Books, 1963. [145]
5. The Standards Western Automatic Computer (later abbreviated to SWAC) [147]
6. The quotation appears in John Hutchins, “From First Conception to First
Demonstration: The Nascent Years of Machine Translation, 1947–1954. A chronology,”
Machine Translation, Vol. 12 No. 3, pp. 195–252, 1997. (A corrected 2005 version, with
minor additions, appears at http://www.hutchinsweb.me.uk/MTJ-1997-corr.pdf.) [147]
7. W. N. Locke and A. D. Booth (eds.), Machine Translation of Languages: Fourteen
Essays, pp. 15–23, Cambridge, MA: MIT Press, 1955. [148]
8. For a history of MT, see W. John Hutchins, “Machine Translation: A Brief History,” in
E. F. K. Koerner and R. E. Asher (eds.), Concise History of the Language Sciences: From
the Sumerians to the Cognitivists, pp. 431–445, Oxford: Pergamon Press, 1995. (Also
available online at http://www.hutchinsweb.me.uk/ConcHistoryLangSci-1995.pdf.)
Hutchins also has a Web page devoted to his publications at
http://ourworld.compuserve.com/homepages/WJHutchins/#History1. [148]
9. For reports about this conference see E. Reifler, “The First Conference on Mechanical
Translation,” Mechanical Translation, Vol. 1 No. 2, pp. 23–32, 1954, and A. C. Reynolds,
“The Conference on Mechanical Translation Held at MIT, June 17–20, 1952,” Mechanical
Translation, Vol. 1, No. 3, pp. 47–55, 1954. [148]
10. http://www-03.ibm.com/ibm/history/exhibits/701/701 translator.html. [148]
11. For a summary of the IBM–Georgetown work, see W. John Hutchins, “The
Georgetown–IBM Experiment Demonstrated in January 1954,” in Robert E. Frederking and
Kathryn B.Taylor (eds.), Proceedings of Machine Translation: From Real Users to Research,
6th Conference of the Association for Machine Translation in the Americas, AMTA-2004,
pp. 102–114, Washington DC, USA, September 28–October 2, 2004, Berlin: Springer, 2004.
An online version is available at http://www.hutchinsweb.me.uk/ATMA-2004.pdf. [149]
12. Yehoshua Bar-Hillel, “The Present Status of Automatic Translation of Languages,”
Advances in Computers, Vol. 1, No. 1, pp. 91–163, 1960. [149]
13. In D. V. Blake and A. M. Uttley (eds.), Proceedings of the Symposium on Mechanisation
of Thought Processes, p. 85, London: Her Majesty’s Stationary Office, 1959. [150]
14. John R. Pierce et al., Language and Machines: Computers in Translation and
Linguistics, ALPAC Report, National Academy of Sciences Publication 416, National
Research Council, Washington, DC, 1966. [150]
15. See http://www.aclweb.org/. [150]
16. Bert F. Green Jr., Alice K. Wolf, Carol Chomsky, and Kenneth Laughery, “BASEBALL:
An Automatic Question Answerer,” pp. 219–224, Proceedings of the Western Joint
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![7 NOTES
Computer Conference, May 1961. Reprinted in Edward A. Feigenbaum and Julian Feldman
(eds.), Computers and Thought, pp. 207–216, New York: McGraw Hill, 1963, and in B.
Grosz, K. Spark Jones, and B. Lynn Webber (eds.), Readings in Natural Language
Processing, Morgan Kaufman, Los Altos, CA, 1986. [151]
17. Ibid. [151]
18. See Robert K. Lindsay, “Inferential Memory as the Basis of Machines Which
Understand Natural Language,” in Edward A. Feigenbaum, and Julian Feldman, op. cit.,
pp. 217–233. [151]
19. Charles K. Ogden, Basic English: A General Introduction with Rules and Grammar,
4th edition, London: Kegan, Paul, Trench, Trubner & Co., Ltd., 1933. (Lindsay says 1,700
words; other sources say 850.) [152]
20. From http://www.cs.utexas.edu/users/ai-lab/simmons.html. [152]
21. Robert F. Simmons, “Synthex,” Communications of the ACM, Vol. 4 , No. 3, p. 140,
March 1961. [152]
22. From “Text Retrieval Online: Historical Perspective on Web Search Engines,” by Trudi
Bellardo Hahn, ASIS Bulletin, April/May 1998. Available online at
http://www.asis.org/Bulletin/Apr-98/hahn.html. [152]
23. Daniel G. Bobrow, “Natural Language Input for a Computer Problem Solving System,”
MIT Artificial Intelligence Project Memo 66, Memorandum MAC-M-148, March 30, 1964.
Available online at
http://dspace.mit.edu/bitstream/handle/1721.1/5922/AIM-066.pdf?sequence=2. An article
based on the dissertation is Chapter 3 of Marvin Minsky (ed.), Semantic Information
Processing, Cambridge, MA: MIT Press, 1968. [152]
154
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![8.2 Early AI Laboratories
machine.2
Working initially with Ed Fredkin at Bolt, Beranek, and Newman
(BBN) and later with others, McCarthy developed an early time-sharing
system at MIT using a DEC PDP-1 computer.3
8.2 Early AI Laboratories
In 1955, Newell moved from the RAND Corporation to Carnegie Tech (which
became Carnegie Mellon University, CMU, in 1967) to work on a Ph.D. degree
in industrial management under Herb Simon. After completing his degree,
Newell stayed on as a professor at Carnegie, and he and Simon began advising
a number of Ph.D. students – using the phrase “complex information
processing (CIP)” to describe their work. (For several years they avoided the
AI sobriquet.) In the fall of 1956, Herb Simon took delivery of an IBM 650,
which was the first computer used for CIP work. Later, they used an IBM 704,
followed by a series of DEC machines.
John McCarthy moved from Dartmouth to MIT in the fall of 1958.
Minsky joined MIT a year later. As Minsky puts it,4
[McCarthy and I] were walking down the hall and we met Jerry
Wiesner or Zimmerman or someone and he said how’s it going and
we said well, we’re working on these artificial intelligence ideas but
we need a little more room and support for some graduate
students. So then a room appeared a few days later. . .
The “room” soon developed into the MIT Artificial Intelligence Project.
Initially, the group used MIT’s IBM 704 computer, which proved not to have
sufficient memory for the programs being written. So it began to use a DEC
PDP-1 belonging to BBN. With funding from another project at MIT, it
bought its own PDP-1, which was followed by the PDP-6 and PDP-10.
Several of the group’s Ph.D. students did their work at BBN and at the
nearby Lincoln Laboratory where Oliver Selfridge continued his AI research –
mainly on pattern recognition and machine learning. In 1962, McCarthy
moved to Stanford where he began an AI project. Seymour Papert (1928– ), a
mathematician who had worked with Jean Piaget, joined Minsky as
co-director of the AI Lab in 1963.
By 1965 at Stanford, McCarthy and colleagues had created a time-sharing
system, called Thor, on a PDP-1 computer. It included twelve Philco display
terminals, which made it the first display-oriented time-sharing system
anywhere in the world.
With the help of Lester Earnest (1930– ), who had moved to Stanford
from Lincoln Laboratory, McCarthy set up the Stanford AI Laboratory (SAIL)
in 1965. Outgrowing its on-campus facilities, SAIL moved to a building in the
Stanford foothills during the summer of 1966. (See Fig. 8.1.) With additional
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![8 1960s’ Infrastructure
In January 1965, Michie became the Director of the UK’s first AI
laboratory, the Experimental Programming Unit, at the University of
Edinburgh. This group was to become the Department of Machine Intelligence
and Perception in October 1966. Michie recruited some top-flight computer
talent, including Rod Burstall, Robin Popplestone, and John Collins. Those
three developed a list-processing language called POP-2, which was the
language used for AI program-writing by members of the Unit. (I’ll describe
some of these programs later.) For many years, Michie’s group worked
collaboratively with a nearby University of Edinburgh group, the
Metamathematics Unit under Bernard Meltzer (circa 1916–2008). The
Metamathematics Unit is famous for the work of Robert Boyer and J Strother
Moore in mechanized theorem proving and of Robert Kowalski in developing
some of the principles of logic programming.8
At IBM in Poughkeepsie, Nathan Rochester and Herb Gelernter continued
AI research for a short time after the Dartmouth workshop. This research
resulted in the geometry-theorem-proving machine. However, soon after,
according to a book about government support for computing research, “in
spite of the early activity of Rochester and other IBM researchers, the
corporation’s interest in AI cooled. Although work continued on
computer-based checkers and chess, an internal report prepared about 1960
took a strong position against broad support for AI.”9
Perhaps IBM wanted to
emphasize how computers helped people perform tasks rather than how they
might replace people. McCarthy’s view about all of this is that “IBM thought
that artificial intelligence [that machines were as smart as people] was bad for
IBM’s image. . . This may have been associated with one of their other image
slogans, which was ‘data processing, not computing.’”10
8.3 Research Support
As the computing systems needed for AI research became larger and more
expensive, and as AI laboratories formed, it became necessary to secure more
financial support than was needed in the days when individual investigators
began work in the field. Two of the major sources of funding during the late
1950s and early 1960s were the Office of Naval Research (ONR) and the
Advanced Research Projects Agency (ARPA), each a part of the U.S. defense
establishment.
ONR was formed shortly after the end of the Second World War. Its
mission was “to plan, foster, and encourage scientific research in recognition of
its paramount importance as related to the maintenance of future naval power
and the preservation of national security.” Its Information Systems Branch was
set up in the mid-1950s under the direction of Marshall Yovits. The branch
supported AI work at several institutions and also sponsored conferences and
workshops on self-organizing systems, cybernetics, optical character
160
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![8.3 Research Support
recognition, and artificial intelligence. All of this was done in anticipation that
these technologies would be generally useful to the U.S. Navy. (A later
director, Marvin Denicoff, supported some of my research and my AI textbook
writing.)
The formation of ARPA was, in part, a response to the successful launch
of the Soviet satellite Sputnik in 1957. ARPA’s mission was to provide
significant amounts of research funds to attack problem areas important to
U.S. defense. One of its most important projects in the late 1950s was the
development of ablative nose cones to absorb and dissipate heat during
ballistic missile reentry. Its Information Processing Techniques Office (IPTO)
was set up in 1962 under the direction of J. C. R. (Lick) Licklider (1915–1990;
Fig. 8.4).
“Lick” (as he was called by all who knew him) was a psychoacoustician
who worked first at Lincoln Laboratory and MIT and later at BBN. Lick’s
1960 paper, “Man-Computer Symbiosis,” proposed that men and computers
should “cooperate in making decisions and controlling complex situations
without inflexible dependence on predetermined programs.”11
Lick was persuaded that computers would play a very important role in
defense – especially in those applications in which people and computers
worked together. At ARPA, he provided funds to MIT for the formation of
Project MAC (an acronym for Machine-Aided Cognition and perhaps for
Multi-Access Computing or Man And Computers). [Project MAC, initially
founded in July 1963, was later to become the Laboratory for Computer
Science (LCS), and still later to evolve into the Computer Science and
Artificial Intelligence Laboratory (CSAIL).] Project MAC took Minsky and
McCarthy’s Artificial Intelligence Project under its wing and also supported
the development of MIT’s Compatible Time-Sharing System (CTSS) under
Fernando Corbató. (CTSS work was separate from McCarthy’s time-sharing
project.)
ARPA funds helped to establish “centers of excellence” in computer
science. Besides MIT, these centers included Stanford, Carnegie Mellon, and
SRI. ARPA also supported computer science work at the RAND Corporation,
the Systems Development Corporation, and BBN, among others. AI was just
one of ARPA’s interests. IPTO also supported research that led to graphical
user interfaces (and the mouse), supercomputing, computer hardware and
very-large-scale integrated circuits (VLSI), and, perhaps most famously,
research that led to the Internet. According to Licklider, “ARPA budgets did
not even include AI as a separate line item until 1968.”12
But as far as AI was concerned, Lick believed that Newell and Simon,
Minsky, and McCarthy ought to be provided with research funds adequate to
support big AI projects. With regard to the situation at Stanford (and
probably to that at MIT and CMU also), Paul Edwards explained that13
[F]unding from ARPA was virtually automatic; Licklider simply
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![8 NOTES
experience with heuristic search and other techniques for manipulating and
using that knowledge. Also, researchers now had new programming languages,
IPL, LISP, and POP-2, that made it easier to write symbol-processing programs.
Complementing all of this symbol-processing technology were neural networks
and related statistical approaches to pattern recognition. These technical
assets, along with the organizational and financial ones, provided a solid base
for the next stage of AI’s development.
Notes
1. For McCarthy’s own history of the development of LISP, see
http://www-formal.stanford.edu/jmc/history/lisp.html. Also see Herbert Stoyan’s history of
LISP at http://www8.informatik.uni-erlangen.de/html/lisp-enter.html. [156]
2. See McCarthy’s memo proposing how to build a time-sharing system at
http://www-formal.stanford.edu/jmc/history/timesharing-memo.html. [157]
3. For more about these early days of computing at MIT and of time-sharing work there
(among other things), see the interview with John McCarthy conducted by William Aspray
of the Charles Babbage Institute on March 2, 1989. It is available online at
http://www.cbi.umn.edu/oh/display.phtml?id=92. [157]
4. From an interview conducted by Arthur L. Norberg on November 1, 1989, for the
Charles Babbage Institute. Available online at
http://www.cbi.umn.edu/oh/display.phtml?id=107. [157]
5. For a history of AI work in the lab up to 1973, see Lester Earnest (ed.), “Final Report:
The First Ten Years of Artificial Intelligence Research at Stanford,” Stanford Artificial
Intelligence Laboratory Memo AIM-228 and Stanford Computer Science Department Report
No. STAN-CS-74-409, July 1973. (Available online at
http://www-db.stanford.edu/pub/cstr/reports/cs/tr/74/409/CS-TR-74-409.pdf.) For other
SAIL history, see “SAIL Away” by Les Earnest at
http://www.stanford.edu/∼learnest/sailaway.htm. [158]
6. A textscript of the interview can be found online at
http://www.aiai.ed.ac.uk/events/ccs2002/CCS-early-british-ai-dmichie.pdf. [158]
7. Donald Michie, “Experiments on the Mechanisation of Game Learning: 1.
Characterization of the Model and its Parameters,” Computer Journal, Vol. 1, pp. 232–263,
1963. [158]
8. For a history of these Edinburgh groups, see Jim Howe’s online 1994 article “Artificial
Intelligence at Edinburgh University: A Perspective” at
http://www.dai.ed.ac.uk/AI at Edinburgh perspective.html. [160]
9. National Research Council, Funding a Revolution: Government Support for Computing
Research, Washington, DC: National Academy Press, 1999. (An html version of this book,
which contains a rather conservative account of AI history, is available from
http://www.nap.edu/catalog.php?record id=6323#toc.) [160]
10. From “An Interview with John McCarthy,” conducted by William Aspray on 2 March
1989, Palo Alto, CA, Charles Babbage Institute, The Center for the History of Information
Processing, University of Minnesota, Minneapolis. [160]
11. J. C. R. Licklider, “Man–Computer Symbiosis,” IRE Transactions on Human Factors in
Electronics, HFE-1, pp. 4–11, 1960. Available online at http://memex.org/licklider.html.
[161]
164
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![8.4 NOTES
12. J. C. R. Licklider, “The Early Years: Founding IPTO,” p. 220 in Thomas C. Bartee
(ed.), Expert Systems and Artificial Intelligence: Applications And Management,
Indianapolis: Howard W. Sams, 1988. [161]
13. Paul Edwards, The Closed World: Computers and the Politics of Discourse in Cold
War America, p. 270, Cambridge, MA: MIT Press, 1996. [161]
14. From “An Interview with John McCarthy,” op. cit. [162]
15. Lester Earnest (ed.), “Final Report: The First Ten Years of Artificial Intelligence
Research at Stanford,” Stanford Artificial Intelligence Laboratory Memo AIM-228 and
Stanford Computer Science Department Report No. STAN-CS-74-409, July 1973. (Available
online at http://www-db.stanford.edu/pub/cstr/reports/cs/tr/74/409/CS-TR-74-409.pdf.)
[162]
16. 12th National Meeting of the Operations Research Society (ORSA) in Pittsburgh. [163]
17. The published version of this talk is in Herbert Simon and Allen Newell, “Heuristic
Problem Solving: The Next Advance in Operations Research,” Operations Research, Vol. 6,
January–February 1958. [163]
18. Hubert L. Dreyfus, “Overcoming the Myth of the Mental,” Topoi, Vol. 25, pp. 43–49,
2006. [163]
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![9 Computer Vision
of monkeys.4
Hubel and Wiesel were awarded the Nobel Prize in Physiology or
Medicine in 1981 (jointly with Roger Sperry for other work).5
As I’ll describe in later sections, computer vision researchers were
developing methods for extracting lines (both large and small) from images.
Hubel and Wiesel’s work helped to confirm their view that finding lines in
images was an important part of the visual process. Yet, straight lines seldom
occur in the natural environments in which cats (and humans) evolved, so why
do they (and we) have neurons specialized for detecting them? In fact, in
1992 the neuroscientists Horace B. Barlow and David J. Tolhurst wrote a
paper titled “Why Do You Have Edge Detectors?”6
As a possible answer to
this question, Anthony J. Bell and Terrence J. Sejnowski later showed
mathematically that natural scenes can be analyzed as a weighted summation
of small edges even though the scenes themselves do not have obvious edges.7
9.2 Recognizing Faces
In the early 1960s at his Palo Alto company, Panoramic Research, Woodrow
(Woody) W. Bledsoe (who later did work on automatic theorem proving at
the University of Texas), along with Charles Bisson and Helen Chan (later
Helen Chan Wolf), developed techniques for face recognition supported by
projects from the CIA.8
Here is a description of their approach taken from a
memorial article:9
This [face-recognition] project was labeled man-machine because
the human extracted the coordinates of a set of features from the
photographs, which were then used by the computer for
recognition. Using a GRAFACON, or RAND TABLET, the
operator would extract the coordinates of features such as the
center of pupils, the inside corner of eyes, the outside corner of
eyes, point of widows peak, and so on. From these coordinates, a
list of 20 distances, such as width of mouth and width of eyes, pupil
to pupil, were computed. These operators could process about 40
pictures an hour. When building the database, the name of the
person in the photograph was associated with the list of computed
distances and stored in the computer. In the recognition phase, the
set of distances was compared with the corresponding distance for
each photograph, yielding a distance between the photograph and
the database record. The closest records are returned.
Bledsoe continued this work with Peter Hart at SRI after leaving Panoramic in
1966.10
Then, in 1970, a Stanford Ph.D. student, Michael D. Kelly, wrote a
computer program that was able automatically to detect facial features in
172
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![9.3 Computer Vision of Three-Dimensional Solid Objects
Figure 9.2: Detecting changes in intensity. (Photographs used with permission
of Lawrence Roberts.)
Figure 9.3: Producing the final line drawing. (Photographs used with permission
of Lawrence Roberts.)
solid objects, much of the subsequent work dealt with toy blocks (or “bricks”
as they are called in Britain).
9.3.2 The “Summer Vision Project”
Interestingly, Larry Roberts was a student of MIT information theory
professor Peter Elias, not of Marvin Minsky. But Minsky’s group soon began
to work on computer vision also. In the summer of 1966, the mathematician
and psychologist Seymour Papert, a recent arrival at MIT’s Artificial
Intelligence Group, launched a “summer vision project.” Its goal was to
develop a suite of programs that would analyze a picture from a “videsector”
(a kind of scanner) to “actually name objects [such as balls, cylinders, and
blocks] by matching them with a vocabulary of known objects.” One
motivation for the project was “to use our summer workers effectively in the
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![9.3 Computer Vision of Three-Dimensional Solid Objects
theory “explains several basic psychophysical findings, and. . . forms the basis
for a physiological model of simple [nerve] cells.”
Marr’s promising career in vision research ended when he succumbed to
cancer in 1980. During the last years of his life he completed an important
book detailing his theories of human vision.21
I’ll describe some of Marr’s
ideas about other visual processing steps in a subsequent chapter.
9.3.4 Processing Line Drawings
Assuming, maybe somewhat prematurely, that low-level vision routines could
produce a line-drawing version of an image, many investigators moved on to
develop methods for analyzing line drawings to find objects in images.
Adolfo Guzman-Arenas (1943– ), a student in Minsky’s AI Group,
focused on how to segment a line drawing of a scene containing blocks into its
constituents, which Guzman called “bodies.” His LISP program for
accomplishing this separation was called SEE and ran on the MIT AI Group’s
PDP-6 computer.22
The input to SEE was a line-drawing representation of a
scene in terms of its surfaces, lines (where two surfaces came together), and
vertices (where lines came together).
SEE’s analysis of a scene began by sorting its vertices into a number of
different types. For each vertex, depending on its type, SEE connected
adjacent planar surfaces with “links.” The links between surfaces provide
evidence that those surfaces belong to the same body. For example, some links
for a scene analyzed by SEE are shown in Fig. 9.11.
SEE performed rather well on a wide variety of line drawings. For
example, it correctly found all of the bodies in the scene shown in Fig. 9.12.
For most of his work, Guzman assumed that somehow other programs
would produce his needed line drawings from actual images. As he wrote in a
paper describing his research,23
The scene itself is not obtained from a visual input device, or from
an array of intensities of brightness. Rather, it is assumed that a
preprocessing of some sort has taken place, and the scene to be
analyzed is available in a symbolic format. . . in terms of points
(vertices), lines (edges), and surfaces (regions).”
Additionally, Guzman did not concern himself with what might be done
after the scene had been separated into bodies:
. . . it cannot find “cubes” or “houses” in a scene, since it does not
know what a “house” is. Once SEE has partitioned a scene into
bodies, some other program will work on them and decide which of
those bodies are “houses.”
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![9 NOTES
Notes
1. For a thorough treatment, see David Forsyth and Jean Ponce, Computer Vision: A
Modern Approach, Chapter 13, Upper Saddle River, NJ: Prentice Hall, 2003. [169]
2. Lettvin et al., “What the Frog’s Eye Tells the Frog’s Brain,” Proceedings of the IRE,
Vol. 47, No. 11, pp. 1940–1951, 1959. [Reprinted as Chapter 7 in William C. Corning and
Martin Balaban (eds.), The Mind: Biological Approaches to Its Functions, pp. 233–258,
1968.] [171]
3. David H. Hubel and Torsten N. Wiesel, “Receptive Fields, Binocular Interaction and
Functional Architecture in the Cat’s Visual Cortex,” Journal of Physiology, Vol. 160, pp.
106–154, 1962. [171]
4. David H. Hubel and Torsten N. Wiesel, “Receptive Fields and Functional Architecture
of Monkey Striate Cortex, Journal of Physiology, Vol. 195, pp. 215–243, 1968. [172]
5. An interesting account of Hubel’s and Wiesel’s work and descriptions about how the
brain processes visual images can be found in Hubel’s online book Eye, Brain, and Vision at
http://neuro.med.harvard.edu/site/dh/index.html. [172]
6. Horace B. Barlow and D. J. Tolhurst, “Why Do You Have Edge Detectors?,” in
Proceedings of the 1992 Optical Society of America Annual Meeting, Technical Digest Series,
Vol. 23, pp. 172, Albuquerque, NM, Washington: Optical Society of America, 1992. [172]
7. Anthony J. Bell and Terrence J. Sejnowski, “Edges Are the ‘Independent Components’
of Natural Scenes,” Advances in Neural Information Processing Systems, Vol. 9, Cambridge,
MA: MIT Press, 1996. Available online at ftp://ftp.cnl.salk.edu/pub/tony/edge.ps.Z. [172]
8. Woodrow W. Bledsoe and Helen Chan, “A Man–Machine Facial Recognition System:
Some Preliminary Results,” Technical Report PRI 19A, Panoramic Research, Inc., Palo
Alto, CA, 1965. [172]
9. Michael Ballantyne, Robert S. Boyer, and Larry Hines, “Woody Bledsoe: His Life and
Legacy,” AI Magazine, Vol. 17, No. 1, pp. 7–20, 1996. Also available online at
http://www.utexas.edu/faculty/council/1998-1999/memorials/Bledsoe/bledsoe.html. [172]
10. Woodrow W. Bledsoe, “Semiautomatic Facial Recognition,” Technical Report SRI
Project 6693, Stanford Research Institute, Menlo Park, CA, 1968. [172]
11. Michael D. Kelly, Visual Identification of People by Computer, Stanford AI Project,
Stanford, CA, Technical Report AI-130, 1970. [173]
12. http://iccv2007.rutgers.edu/TakeoKanadeResponse.htm. [173]
13. Lawrence G. Roberts, “Machine Perception of Three-Dimensional Solids,” MIT Ph.D.
thesis, 1963; published as Lincoln Laboratory Technical Report #315, May 22, 1963;
appears in J. T. Tippett et al. (eds.), Optical and Electro-Optical Information Processing,
pp. 159–197, Cambridge, MA: MIT Press, 1965. Available online at
http://www.packet.cc/files/mach-per-3D-solids.html. [173]
14. The project is described in MIT’s Artificial Intelligence Group Vision Memo No. 100
available at ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-100.pdf. [176]
15. Russell A. Kirsch et al., “Experiments in Processing Pictorial Information with a Digital
Computer,” Proceedings of the Eastern Joint Computer Conference, pp. 221–229, Institute
of Radio Engineers and Association Association for Computing Machinery, December 1957.
[176]
16. According to Sobel, he and a fellow student, Gary Feldman, first presented the operator
in a Stanford AI seminar in 1968. It was later described in Karl K. Pingle, “Visual
Perception by a Computer,” in A. Grasselli (ed.), Automatic Interpretation and
Classification of Images, pp. 277–284, New York: Academic Press, 1969. It was also
186
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![9.3 NOTES
mentioned in Richard O. Duda and Peter E. Hart, Pattern Classification and Scene
Analysis, pp. 271–272, New York: John Wiley & Sons, 1973. [177]
17. See, for example, M. H. Hueckel, “An Operator Which Locates Edges in Digitized
Pictures,” Journal of the ACM, Vol. 18, No. 1, pp. 113–125, January 1971, and Berthold K.
P. Horn, “The Binford–Horn Line Finder,” MIT AI Memo 285, MIT, July 1971 (revised
December 1973 and available online at
http://people.csail.mit.edu/bkph/AIM/AIM-285-OPT.pdf). [178]
18. David Marr and Ellen Hildreth, “Theory of Edge Detection,” Proceedings of the Royal
Society of London, Series B, Biological Sciences, Vol. 207, No. 1167, pp. 187–217, February
1980. [178]
19. John E. Canny, “A Computational Approach to Edge Detection,” IEEE Transactions
Pattern Analysis and Machine Intelligence, Vol. 8, pp. 679–714, 1986. [180]
20. David Marr, “Early Processing of Visual Information,” Philosophical Transactions of
the Royal Society of London, Series B, Biological Sciences, Vol. 275, No. 942, pp. 483–519,
October 1976. [180]
21. David Marr, Vision: A Computational Investigation into the Human Representation
and Processing of Visual Information, San Francisco: W.H. Freeman and Co., 1982. [181]
22. Guzman’s 1968 Ph.D. thesis is titled “Computer Recognition of Three Dimensional
Objects in a Visual Scene” and is available online at
http://www.lcs.mit.edu/publications/pubs/pdf/MIT-LCS-TR-059.pdf. [181]
23. Adolfo Guzman, “Decomposition of a Visual Scene into Three-Dimensional Bodies,”
AFIPS, Vol. 33, pp. 291–304, Washington, DC: Thompson Book Co., 1968. Available online
as an MIT AI Group memo at
ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-171.pdf. [181]
24. Personal communication, September 14, 2006. [182]
25. David A. Huffman, “Impossible Objects as Nonsense Sentences,” in B. Meltzer and D.
Michie (eds.), Machine Intelligence 6, pp. 195–234, Edinburgh: Edinburgh University Press,
1971, and David A. Huffman, “Realizable Configurations of Lines in Pictures of Polyhedra,”
in E. W. Elcock and D. Michie (eds.), Machine Intelligence 8, pp. 493–509, Chicester: Ellis
Horwood, 1977. [183]
26. According to Wikipedia, this impossible object was first drawn by the Swedish artist
Oscar Reutersvärd in 1934. [183]
27. Max B. Clowes, “On Seeing Things,” Artificial Intelligence, Vol. 2, pp. 79–116, 1971.
[185]
28. David L. Waltz, “Generating Semantic Descriptions from Drawings of Scenes with
Shadows,” MIT AI Lab Technical Report No. AITR-271, November 1, 1972. Available
online at https://dspace.mit.edu/handle/1721.1/6911. A condensed version appears in
Patrick Winston (ed.), The Psychology of Computer Vision, pp. 19–91, New York:
McGraw-Hill, 1975. [185]
29. Personal communication, September 20, 2006. [185]
30. Gilbert Falk, “Computer Interpretation of Imperfect Line Data as a Three-Dimensional
Scene,” Ph.D. thesis in Electrical Engineering, Stanford University, Artificial Intelligence
Memo AIM-132, and Computer Science Report No. CS180, August 1970. Also see Gilbert
Falk, “Interpretation of Imperfect Line Data as a Three-Dimensional Scene,” Artificial
Intelligence, Vol. 3, pp. 101–144, 1972. [185]
31. Gunnar Rutger Grape, “Model Based (Intermediate Level) Computer Vision,” Stanford
Computer Science Ph.D. thesis, Artificial Intelligence Memo AIM-204, and Computer
Science Report No. 266, May 1973. [185]
32. Yoshiaki Shirai, “A Heterarchical Program for Recognition of Polyhedra,” MIT AI
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![9 NOTES
Memo No. 263, June 1972. Available online at
ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-263.pdf. [185]
33. Alan K. Mackworth, “Interpreting Pictures of Polyhedral Scenes,” Artificial Intelligence,
Vol. 4, No. 2, pp. 121–137, June 1973. [185]
188
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![10.1
Chapter 10
“Hand–Eye” Research
The motivation for much of the computer vision research that I have described
during this period was to provide information to guide a robot arm. Because
the images that could be analyzed best were of simple objects such as toy
blocks, work was concentrated on getting a robot arm to stack and unstack
blocks. I’ll describe some typical examples of this “hand–eye” research,
beginning with a project that did not actually involve an “eye.”
10.1 At MIT
A computer-guided mechanical “hand” was developed by Heinrich A. Ernst in
1961 as part of his Electrical Engineering Sc.D. work at MIT.1
(His advisor
was Claude Shannon.) The hand, named MH-1, was a “mechanical
servomanipulator [an American Machine and Foundry model 8] adapted for
operation by the TX-0 computer.” It used tactile sensors mounted on the hand
to guide it because, as Ernst wrote, “organs for vision are too difficult to build
at the present time.” The abstract of Ernst’s thesis describes some of what the
system could do:
[O]ne program consisting of nine statements will make the hand do
the following: Search the table for a box, remember its position,
search the table for blocks, take them and put them into the box.
The position of the objects is irrelevant as long as they are on the
table. If as a test for the built-in mechanical intelligence, the box
should be taken away and placed somewhere else while the hand
searches for blocks, MH-1 will remember the new position of the
box and continue to work with it as soon as it has realized the
change in the situation, that is, has bumped into the box while
looking for blocks. This will be done automatically, without any
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![10.2 At Stanford
Figure 10.1: A block arrangement for the MIT copy demo. (Used with permis-
sion of Berthold Horn.)
use a TV camera and primitive vision routines to locate blocks scattered on a
table. Using the information thus obtained, it could control the arm to sort
the blocks.6
According to the authors,
One section of the system scans the TV image to find the outer
edge of an object, then traces around the outside edges of the
object using a gradient operator [an edge detector] to find the
location and direction of the edge. Curve fitting routines fit
straight lines to a list of points found on the edges and calculate
the position of the corners. . . . A second section of the system is
devoted to control of the arm. . . . The sections which control it
consist of a solution program which calculates the angular position
required at each actuator and a servo program which drives the
arm to the desired positions. . .
Les Earnest wrote that this was “the first robotics visual feedback
system,” although “only the outer edges of the blocks were observed, [and] the
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![10 “Hand–Eye” Research
hand had to be removed from view when visual checking was done. . . .”7
Several versions of this block-sorting and stacking system were
demonstrated. In one, the system located colored blocks on a table and placed
them in separate stacks of red and blue blocks.8
By 1971, a vision-guided block stacking system solved the “instant
insanity” puzzle.9
In that puzzle, four cubes, each face of which has one of
four different colors, must be arranged in a tower such that each side of the
tower shows four different colors. The system, running on SAIL’s PDP-10
computer, used a TV camera equipped with a turret of four lenses and a color
wheel to locate four cubes on a table top. The arm picked up and turned each
cube to expose all faces to the camera. Then, knowing the color of each face
and having found a solution to the puzzle, the arm stacked the cubes in a
tower exhibiting the solution. [A silent, 16-mm color six-minute film, titled
Instant Insanity, was made by Richard Paul and Karl Pingle in August 1971
and shown at the second International Joint Conference on Artificial
Intelligence (IJCAI) in London. The film can be seen at
http://www.youtube.com/watch?v=O1oJzUSITeY. ]
Dabblal Rajagopal “Raj” Reddy (1937– ; Fig. 10.2) was the first Ph.D.
student of Stanford’s new Department of Computer Science. His thesis
research was on speech recognition. After obtaining his Ph.D. in 1966, Reddy
joined Stanford’s faculty and continued research on speech recognition at
SAIL. While there he participated in a project to control a hand–eye system
by voice commands.10
As stated in a project review, “Commands as
complicated as ‘Pick up the small block in the lower lefthand corner,’ are
recognized and the tasks are carried out by the computer controlled arm.”
(The system was demonstrated in a 1969 fifteen-minute, 16-mm color, sound
film showing some of Reddy’s results on speech recognition. It is titled Hear
Here and was produced by Raj Reddy, Dave Espar, and Art Eisensen. The
film is available at http://www.archive.org/details/sailfilm hear.) In 1969
Reddy moved to CMU where he pursued research in speech recognition and
later became Dean of CMU’s School of Computer Science.
In the early 1970s, the Stanford team used a vision system and a new
electromechanical hand designed by mechanical engineering student Vic
Scheinman11
to assemble a model “T” Ford racing water pump.12
An
industrial-style setup was used with tools in fixed places, screws in a feeder,
and the pump body and cover on a pallet. A diagram of the workspace is
shown in Fig. 10.3.
The hand–eye system executed the following complex set of steps that
was computed previously:
locate the pump base, move it into standard position, determine
the final grasping position by touch, place the pump base in its
standard position, insert two pins to guide the alignment of the
gasket and cover, put on the gasket, visually check the position of
192
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![10 “Hand–Eye” Research
Figure 10.5: FREDDY II, the University of Edinburgh robot. (Photograph
courtesy of University of Edinburgh.)
language. The robot motors and cameras were controlled by a Honeywell
H316, with 4k 16-bit words (later upgraded to 8k words, at a cost of about
$8,000 for the additional 8k bytes!).
Harry Barrow (1943– ), a key person involved in the work, later gave this
account of FREDDY’s operation:16
[A]cquiring an image from the TV camera took quite a few seconds
and processing took even longer, and in a single run FREDDY
took between 100 and 150 pictures! It took a picture every time it
picked up an object to check it has successfully lifted it and not
dropped it, and it took a picture every time it put an object down
to verify the space was empty. It also scanned the entire world
(which required multiple pictures) several times to make a map,
196
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![10.4 NOTES
and it looked at each object from two different cameras to do some
stereo-style estimation of position and size. In fact, the system
made the most intensive use of image data of any robot system in
the world. The Stanford and MIT systems only took a very small
number of pictures to perform their tasks, and relied heavily on
dead reckoning and things not going wrong. We, on the other
hand, assumed that things were likely to go wrong (objects
dropped, rolling, etc.) and made our system highly robust. I really
believe that in many ways it was probably the most advanced
hand–eye system in existence at the time.
FREDDY is now on permanent exhibition in the Royal Scottish Museum
in Edinburgh, with a continuous-loop movie of FREDDY assembling the
mixed model car and boat kits.
Hand–eye research at Edinburgh was suspended during the mid-1970s in
part owing to an unfavorable assessment of its prospects in a study
commissioned by the British Science Research Council. (I’ll have more to say
about that assessment later.)
Notes
1. Heinrich A. Ernst, “MH-1, A Computer-Operated Mechanical Hand,” Sc.D. thesis,
Massachusetts Institute of Technology, Department of Electrical Engineering, 1962.
Available online at
http://dspace.mit.edu/bitstream/handle/1721.1/15735/09275630.pdf?sequence=1. [189]
2. Reported in Berthold K. P. Horn, “The Binford–Horn Line Finder,” MIT AI Memo 285,
MIT, July 1971 (revised December 1973). Available online at
http://people.csail.mit.edu/bkph/AIM/AIM-285-OPT.pdf. [190]
3. Patrick Winston gives a nice description of the MIT programs just mentioned (including
the copy demo and Winston’s own thesis work on learning structural descriptions of object
configurations) in Patrick H. Winston, “The MIT Robot,” Machine Intelligence 7, Bernard
Meltzer and Donald Michie (eds.), pp. 431–463, New York: John Wiley and Sons, 1972.
[190]
4. For a brief review, see Lester Earnest (ed.), “Final Report: The First Ten Years of
Artificial Intelligence Research at Stanford,” Stanford Artificial Intelligence Laboratory
Memo AIM-228 and Stanford Computer Science Department Report No. STAN-CS-74-409,
July 1973. (Available online at
http://www-db.stanford.edu/pub/cstr/reports/cs/tr/74/409/CS-TR-74-409.pdf.) For more
details see Jerome A. Feldman et al., “The Stanford Hand–Eye Project,” Proceedings of the
IJCAI, pp. 521–526, Washington, DC, 1969, and Jerome A. Feldman et al., “The Stanford
Hand-Eye Project – Recent Results,” presented at IFIP Congress, Stockholm, 1974. [190]
5. John McCarthy, private communication, August 11, 2007. [190]
6. The system is described in Karl K. Pingle, Jonathan A. Singer, and William M.
Wichman, “Computer Control of a Mechanical Arm through Visual Input,” Proceedings of
the IFIP Congress (2), pp. 1563–1569, 1968. The vision part of the system is described in
William Wichman, “Use of Optical Feedback in the Computer Control of an Arm,” Stanford
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![10 NOTES
Electrical Engineering Department Engineers thesis, August 1967 (and also appears as
Stanford Artificial Intelligence Memo AIM-56, 1967.) [191]
7. Lester Earnest, op. cit.. [192]
8. Butterfinger, an 8-minute, 16-mm color film showing a version of this sorting system in
operation was produced and directed by Gary Feldman in 1968. The film is available at
http://projects.csail.mit.edu/films/aifilms/digitalFilms/1mp4/09-robot.mp4. [192]
9. The system is described in Jerome Feldman et al., “The Use of Vision and Manipulation
to Solve the ‘Instant Insanity’ Puzzle,” Proceedings of the IJCAI, pp. 359–364, London:
British Computer Society, September 1971. [192]
10. The system is described in Les Earnest et al., “A Computer with Hands, Eyes, and
Ears,” Proceedings of the 1968 Fall Joint Computer Conference, Washington, DC:
Thompson, 1968. [192]
11. Victor D. Scheinman, “Design of a Computer Manipulator,” Stanford AI Memo AIM-92,
June 1, 1969. [192]
12. This task is described in Robert Bolles and Richard Paul, “The Use of Sensory Feedback
in a Programmable Assembly System,” Stanford AI Laboratory Memo AIM-220, Stanford
Computer Science Department Report STAN-CS-396, October 1973, which is available
online at ftp://reports.stanford.edu/pub/cstr/reports/cs/tr/73/396/CS-TR-73-396.pdf.
[192]
13. See Masakazu Ejiri et al., “An Intelligent Robot with Cognition and Decision-Making
Ability,” Proceedings of the IJCAI, pp. 350–358, London: British Computer Society,
September 1971, and Masakazu Ejiri et al., “A Prototype Intelligent Robot That Assembles
Objects from Plan Drawings,” IEEE Transactions on Computers, Vol. 21, No. 2, pp.
161–170, February 1972. [193]
14. This is an abbreviation, according to Donald Michie, of Frederick, an acronym of
Friendly Robot for Education, Discussion and Entertainment, the Retrieval of Information,
and the Collation of Knowledge. [194]
15. http://www.aiai.ed.ac.uk/project/freddy/. The key reference is A. P. Ambler, H. G.
Barrow, C. M. Brown, R. M. Burstall, and R. J. Popplestone, “A Versatile
Computer-Controlled Assembly System,” Artificial Intelligence, Vol. 6, pp. 129–156, 1975.
[195]
16. E-mail note from Harry Barrow of January 3, 2009. [196]
198
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![11 Knowledge Representation and Reasoning
11.1 Deductions in Symbolic Logic
Aristotle got things started in logic with his analysis of syllogisms. In the
nineteenth century, George Boole developed the foundations of propositional
logic, and Gottlob Frege improved the expressive power of logic by proposing a
language that could include internal components (called “terms”) as part of
propositions. Later developments by various logicians gave us what we call
today the predicate calculus – the very language in which McCarthy proposed
to represent the knowledge needed by an intelligent system.
Here is an instance of one of Aristotle’s syllogisms, stated in the language
of the predicate calculus:
1. (∀x)[Man(x) ⊃ Mortal(x)]
(The expression “(∀x)” is a way of writing “for all x”; and the
expression “⊃” is a way of writing “implies that.” “Man(x)” is a
way of writing “x is a man”; and “Mortal(x)” is a way of writing
“x is mortal.” Thus, the entire expression is a way of writing “for
all x, x is a man implies that x is mortal” or, equivalently, “all men
are mortal.”)
2. Man(Socrates)
(Socrates is a man.)
3. Therefore, Mortal(Socrates)
(Socrates is mortal.)
Statement 3, following “Therefore,” is an example of a deduction in logic.
McCarthy proposed that the knowledge that an intelligent agent might need in
a specific situation could be deduced from the general knowledge given to it
earlier. Thus, for McCarthy-style AI, not only do we need a language (perhaps
that of the predicate calculus) but a way to make the necessary deductions
from statements in the language.
Logicians have worked out a variety of deduction methods based on what
they call “rules of inference.” For example, one important inference rule is
called modus ponens (Latin for “mode that affirms”). It states that if we have
the two logical statements P and P ⊃ Q, then we are justified in deducing the
statement Q.
By the 1960s programs had been written that could use inference rules to
prove theorems in the predicate calculus. Chief among these were those of
Paul Gilmore at IBM,1
Hao Wang at IBM,2
and Dag Prawitz,3
now at
Stockholm University. Although their programs could prove simple theorems,
proving more complex ones would have required too much search.4
A Harvard Ph.D. student, Fisher Black (1938–1995), later a co-inventor of
the Black–Scholes equation for pricing options,5
had done early work
200
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![11 Knowledge Representation and Reasoning
another Stanford student, Robert Yates) programmed, in LISP, a
resolution-based deduction system called QA3, which ran on SRI’s time-shared
SDS 940 computer. (QA1, Green’s first effort, guided by Bertram Raphael at
SRI, was an attempt to improve on Raphael’s earlier SIR system. QA2 was
Green’s first system based on resolution, and QA3 was a more sophisticated
and practical descendant.) “QA” stood for “question answering,” one of the
motivating applications.
I’ll present a short illustrative example of QA3’s question-answering
ability taken from Green’s Stanford Ph.D. thesis.10
First, two statements are
given to the system, namely,
1. ROBOT(Rob)
(Rob is a robot.)
2. (∀x)[MACHINE(x) ⊃ ¬ANIMAL(x)]
(x is a machine implies that it is not an animal.)
The system is then asked “Is everything an animal?” by having it attempt to
deduce the statement
3. (∀x)ANIMAL(x)
QA3 not only answers “NO,” finding that such a deduction is impossible, but it
also gives a “counterexample” as an answer to the question:
4. x = Rob
(This indicates that ¬ANIMAL(Rob) contradicts what was to be
deduced.)
The use of resolution, like that of any inference rule, to deduce some specific
conclusion from a large body of logical statements involves the need to decide
to which two statements, among the many possibilities, the rule should be
applied. Then a similar decision must be made again and again until, one
hopes, finally the desired conclusion is obtained. So just as with programs for
playing checkers, solving puzzles, and proving geometry theorems, deduction
programs are faced with the need to try many possibilities in their search for a
solution. As with those other programs, various heuristic search methods have
been developed for deduction programs.
11.2 The Situation Calculus
Green realized that “question answering” was quite a broad topic. One could
ask questions about almost anything. For example, one could ask “What is a
202
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![11.3 Logic Programming
program for rearranging a list of numbers so that they are in increasing
numerical order?” Or one could ask, “What is the sequence of steps a robot
should take to assemble a tower of toy blocks?” The key to applying QA3 to
answer questions of this sort lay in using McCarthy’s “situation calculus.”
McCarthy proposed a version of logic he called the “situation calculus” in
which one could write logical statements that explicitly named the situation in
which something or other was true. For example, one toy block may be on top
of another in one situation but not in another. Green developed a version of
McCarthy’s logic in which the situation, in which something was true,
appeared as one of the terms in an expression stating that something was true.
For example, to say that block A is on top of block B in some situation S
(allowing for the fact that this might not be the case in other situations),
Green would write
On(A, B, S),
to say that block A is blue in all situations, Green would write
(∀s)Blue(A, s),
and to say that there exists some situation in which block A is on block B,
Green would write
(∃s)On(A, B, s).
Here “(∃s)” is a way of writing “there exists some s such that . . . ”
Not only was QA3 able to deduce statements, but when it deduced a
so-called existential statement (such as the one just mentioned), it was able to
compute an instance of what was alleged to “exist.” Thus, when it deduced
the statement (∃s)On(A, B, s), it also computed for which situation the
deduction was valid. Green devised a way in which this value could be
expressed in terms of a list of actions for a robot that would change some
initial situation into the situation for which the deduced statement was true.
Thus, for example, QA3 could be used to plan courses of action for a robot.
Later, we’ll see how it was used for this purpose.
11.3 Logic Programming
In the same way that QA3 could be used to make robot plans, it could also
construct simple computer programs. In his 1969 paper, Green wrote
The formalization given here [can] be used to precisely state and
solve the problem of automatic generation of programs, including
recursive programs, along with concurrent generation of proofs of
the correctness of these programs. Thus any programs
automatically written by this method have no errors.
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![11 NOTES
away from “trying to represent knowledge as collections of separate, simple
fragments” such as sentences in a logical language. As he defined them,
A frame is a data-structure for representing a stereotyped
situation, like being in a certain kind of living room, or going to a
child’s birthday party. Attached to each frame are several kinds of
information. Some of this information is about how to use the
frame. Some is about what one can expect to happen next. Some
is about what to do if these expectations are not confirmed.
. . .
Collections of related frames are linked together into frame-systems.
The effects of important actions are mirrored by transformations
between the frames of a system. These are used to make certain
kinds of calculations economical, to represent changes of emphasis
and attention, and to account for the effectiveness of “imagery.”
Minsky’s paper described how frame systems could be applied to vision
and imagery, linguistic and other kinds of understanding, memory acquisition,
retrieval of knowledge, and control. Although his paper was rich in ideas,
Minsky did not actually implement any frame systems. A couple of years
later, some of his students and former students did implement some framelike
systems. One, called FRL (for Frame Representation Language), was developed
by R. Bruce Roberts and Ira P. Goldstein.31
Daniel Bobrow and Terry
Winograd (the latter being one of Papert’s students), implemented a more
ambitious system called KRL (for Knowledge Representation Language).32
Frame systems accommodated a style of reasoning in which details “not
specifically warranted” could be assumed, thus “bypassing “logic,” as Minsky
would have it. This style was already used earlier in Raphael’s SIR system (see
p. 134), and researchers advocating the use of logical languages for knowledge
representation would later extend logic in various ways to accommodate this
style also. Even so, the last section (titled “Criticism of the Logistic
Approach”) of Minsky’s paper about frames gives many reasons why one might
doubt (along with Minsky) “the feasibility of representing ordinary knowledge
effectively in the form of many small, independently ‘true’ propositions.”
Notes
1. Paul C. Gilmore, “A Proof Method for Quantification Theory: Its Justification and
Realization,” IBM Journal of Research and Development, Vol. 4, pp. 28–35, 1960. [200]
2. Hao Wang, “Proving Theorems by Pattern Recognition,” Communications of the ACM,
Vol. 4, No. 3, pp. 229–243, 1960, and Hao Wang, “Toward Mechanical Mathematics,” IBM
Journal of Research and Development, Vol. 4, pp. 2–21, 1960. [200]
210
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![11.5 NOTES
3. D. Prawitz, H. Prawitz, and N. Voghera, “A Mechanical Proof Procedure and Its
Realization in an Electronic Computer,” Journal of the Association for Computing
Machinery, Vol. 7, pp. 102–128, 1960. [200]
4. For additional background and history about automated deduction, see Wolfgang Bibel,
“Early History and Perspectives of Automated Deduction,” in J. Hertzberg, M. Beetz, and
R. Englert (eds.), Proceedings of the 30th Annual German Conference on Artificial
Intelligence (KI-2007), Lecture Notes on Artificial Intelligence, pp. 2–18, Berlin:
Springer-Verlag, 2007. [200]
5. In 1997 Myron Scholes and Robert C. Merton were awarded a Nobel Prize in economics
for their option-pricing work. Black died of cancer in 1995. The Nobel Prize is not given
posthumously; however, in its announcement of the award, the Nobel committee
prominently mentioned Black’s key role. [200]
6. Fischer Black, “A Deductive Question-Answering System,” Ph.D. dissertation, Harvard
University, June 1964. Reprinted in Marvin Minsky (ed.), Semantic Information Processing,
pp. 354–402, Cambridge, MA: MIT Press, 1968. [201]
7. John Alan Robinson, “A Machine-Oriented Logic Based on the Resolution Principle,”
Journal of the ACM, Vol. 12, No. 1, pp. 23–41, 1965. [201]
8. For a description of some of this work, see Larry Wos, Ross Overbeek, Ewing Lusk, and
Jim Boyle, Automated Reasoning: Introduction and Applications, second edition, New York:
McGraw-Hill, 1992. For more recent work, visit Larry Wos’s Web page at
http://www.mcs.anl.gov/∼wos/. [201]
9. William McCune, “Solution of the Robbins Problem,” Journal of Automated Reasoning,
Vol. 19, No. 3, pp. 263–276, 1997. [201]
10. Available as an SRI Technical Note: C. Green, “Application of Theorem Proving to
Problem Solving,” Technical Note 4, AI Center, SRI International, 333 Ravenswood Ave,
Menlo Park, CA 94025, March 1969. Online version available at
http://www.ai.sri.com/pubs/files/tn004-green69.pdf. See also C. Green, “Theorem Proving
by Resolution as a Basis for Question-Answering Systems,” in B. Meltzer and D. Michie,
Machine Intelligence 4, pp. 183ff, Edinburgh: Edinburgh University Press, 1969, and C.
Green, “Applications of Theorem Proving to Problem Solving,” reprinted from a 1969 IJCAI
conference article in B. L. Webber and N. J. Nilsson (eds.), Readings in Artificial
Intelligence, pp, 202–222, San Francisco: Morgan Kaufmann, 1981. [202]
11. Robert A. Kowalski and Donald Kuehner, “Linear Resolution with Selection Function,”
Artificial Intelligence, Vol. 2, Nos. 3–4, pp. 227–260, 1971. [204]
12. From one of Kowalski’s Web pages: http://www.doc.ic.ac.uk/∼rak/history.html. [204]
13. http://www.doc.ic.ac.uk/∼rak/history.html. For Colmerauer and Roussel’s account of
the birth of PROLOG see Alain Colmerauer and Philippe Roussel, “The Birth of PROLOG,
in Thomas J. Bergin and Richard G. Gibson (eds.), Programming Languages, New York:
ACM Press, Addison-Wesley, 1996. Available online at
http://alain.colmerauer.free.fr/ArchivesPublications/HistoireProlog/19november92.pdf.
[204]
14. See Carl Hewitt, “Procedural Embedding of Knowledge in PLANNER,” Proceedings of
the Second International Joint Conference on Artificial Intelligence, pp. 167–182, Los Altos,
CA: Morgan Kaufmann Publishing Co., 1971. [205]
15. John R. Anderson and Gordon H. Bower, Human Associative Memory, Washington,
DC: Winston and Sons, 1973. [205]
16. From the Web site http://rumelhartprize.org/john.htm. [205]
17. Robert F. Simmons, “Semantic Networks: Computation and Use for Understanding
English Sentences,” in Roger Schank and Kenneth Colby (eds.), Computer Models of
Thought and Language, pp. 63–113, San Francisco: W. H. Freeman and Co., 1973. [205]
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![11 NOTES
18. Stuart C. Shapiro, “A Net Structure for Semantic Information Storage, Deduction and
Retrieval,” Proceedings of the Second International Joint Conference on Artificial
Intelligence, pp. 512–523, Los Altos, CA: Morgan Kaufmann Publishing Co., 1971. [205]
19. An early paper is Stuart C. Shapiro, “The SNePS Semantic Network Processing
System,” in Nicholas V. Findler (ed.), Associative Networks: The Representation and Use of
Knowledge by Computers, pp. 179–203, New York: Academic Press, 1979. [205]
20. The SNePS Web page is at http://www.cse.buffalo.edu/sneps/. [206]
21. Roger C. Schank, “A Conceptual Dependency Representation for a Computer-Oriented
Semantics,” Ph.D. thesis, University of Texas at Austin, 1969. Available as Stanford AI
Memo 83 or Computer Science Technical Note 130, Computer Science Department, Stanford
University, Stanford, CA, 1969. [206]
22. Roger C. Schank, “Identification of Conceptualizations Underlying Natural Language,”
in Roger Schank and Kenneth Colby (eds.), Computer Models of Thought and Language, pp.
187–247, San Francisco: W. H. Freeman and Co., 1973. [206]
23. Interested readers might refer to various of his books and papers – for example, Roger
C. Schank, “Conceptual Dependency: A Theory of Natural Language Understanding”
Cognitive Psychology, Vol. 3, pp. 552–631, 1972, and Roger C. Schank, Conceptual
Information Processing, New York: Elsevier, 1975. [206]
24. Roger C. Schank and Robert P. Abelson, Scripts, Plans, Goals, and Understanding: An
Inquiry into Human Knowledge Structures, Hillsdale, NJ: Lawrence Erlbaum Associates,
1977. [207]
25. Ibid. [209]
26. Roger C. Schank, Dynamic Memory: A Theory of Reminding and Learning in
Computers and People, Cambridge: Cambridge University Press, 1982. [209]
27. Roger C. Schank, Dynamic Memory Revisited, Cambridge: Cambridge University Press,
1999. [209]
28. See http://www.edge.org/3rd culture/bios/schank.html. [209]
29. See http://aigp.csres.utexas.edu/∼aigp/researcher/show/192. [209]
30. Marvin Minsky, “A Framework for Representing Knowledge,” MIT AI Laboratory
Memo 306, June 1974. Reprinted in Patrick Winston (ed.), The Psychology of Computer
Vision, New York: McGraw-Hill, 1975. Available online at
http://web.media.mit.edu/∼minsky/papers/Frames/frames.html. [209]
31. R. Bruce Roberts and Ira P. Goldstein, The FRL Primer, Massachusetts Institute of
Technology AI Laboratory Technical Report AIM-408, July 1977; available online at
ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-408.pdf. [210]
32. Daniel G. Bobrow and Terry A. Winograd, “An overview of KRL, a Knowledge
Representation Language,” Report Number CS-TR-76-581, Department of Computer
Science, Stanford University, November 1976. Available online at
ftp://reports.stanford.edu/pub/cstr/reports/cs/tr/76/581/CS-TR-76-581.pdf. Appeared
later as Daniel Bobrow and Terry Winograd, “An Overview of KRL, a Knowledge
Representation Language,” Cognitive Science, Vol. 1, No. 1, pp. 3–46, January 1977. [210]
212
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![12 Mobile Robots
17, 1966. (Coincidentally, just before joining SRI in 1961, I had just finished a
three-year stint of duty as an Air Force Lieutenant at RADC working on
statistical signal-processing techniques for radar systems.) Ruth Davis played
a prominent role in getting ARPA and RADC to move forward on getting the
project started. The “knitting together” of several disparate AI technologies
was one of the primary challenges and one of the major contributions of SRI’s
automaton project.4
One of the tasks was the actual construction of a robot vehicle whose
activities would be controlled by a suite of programs. Because of various
engineering idiosyncrasies, the vehicle shook when it came to an abrupt stop.
We soon called it “Shakey,” even though one of the researchers thought that
sobriquet too disrespectful. [Shakey was inducted into the “Robot Hall of
Fame” (along with C-3PO among others) in 2004.5
It was also named as the
fifth-best robot ever (out of 50) by Wired Magazine in January 2006. Wired’s
numbers 2 and 4 were fictional, “Spirit” and ”Opportunity” (the Mars robots)
were number 3, and “Stanley” (winner of the 2005 DARPA “Grand
Challenge”) was named “the #1 Robot of All Time.” Shakey is now exhibited
at the Computer History Museum in Mountain View, California.]6
Shakey had an on-board television camera for capturing images of its
environment, a laser range finder (triangulating, not time-of-flight) for sensing
its distance from walls and other objects, and cat-whisker-like bump detectors.
Shakey’s environment was a collection of “rooms” connected by doorways but
otherwise separated by low walls that we could conveniently see over but
Shakey could not. Some of the rooms contained large objects, as shown in Fig.
12.3. The size of Shakey can be discerned from inspection of Fig. 12.4.
Most of the programs that we developed to control Shakey were run on a
DEC PDP-10 computer. Between the PDP-10 and the mobile vehicle itself
were a PDP-15 peripheral computer (for handling the lower level
communications and commands to on-board hardware) and a two-way radio
and video link. The PDP-10 programs were organized in what we called a
“three-layer” hierarchy. Programs in the lowest level drove all of the motors
and captured sensory information. Programs in the intermediate level
supervised primitive actions, such as moving to a designated position, and also
processed visual images from Shakey’s TV camera. Planning more complex
actions, requiring the execution of a sequence of intermediate-level actions,
was done by programs in the highest level of the hierarchy. The Shakey
project involved the integration of several new inventions in search techniques,
in robust control of actions, in planning and learning, and in vision. Many of
these ideas are widely used today. The next few subsections describe them.
12.1.1 A∗
: A New Heuristic Search Method
One of the first problems we considered was how to plan a sequence of “way
points” that Shakey could use in navigating from place to place. In getting
216
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![12.1 Shakey, the SRI Robot
would have to push the box under the bananas, climb up on the box, and then
grab the bananas.28
McCarthy is said to have heard Karl Lashley at the 1948
Caltech Hixon symposium describe a similar problem for demonstrating
intelligent problem solving by chimpanzees.
One of the persons who was impressed with Shakey was Bill Gates, who
later co-founded Microsoft. He saw the 1972 Shakey film as a junior in high
school and drove down from Seattle to SRI (with Paul Allen, who would be
the other co-founder of Microsoft) to have a look. According to one source, he
was “particularly excited about Shakey moving things around so it could go
up a ramp.”29
12.1.7 Shakey Runs into Funding Troubles
Shakey was the first robot system having the abilities to plan, reason, and
learn; to perceive its environment using vision, range-finding, and touch
sensors; and to monitor the execution of its plans. It was, perhaps, a bit ahead
of its time. Much more research (and progress in computer technology
generally) would be needed before practical applications of robots with
abilities such as these would be feasible. We mentioned some of the limiting
assumptions that were being made by robot research projects at that time in
one of our reports about Shakey:
Typically, the problem environment [for the robot] is a dull sort of
place in which a single robot is the only agent of change – even
time stands still until the robot moves. The robot itself is easily
confused; it cannot be given a second problem until it finishes the
first, even though the two problems may be related in some
intimate way. Finally, most robot systems cannot yet generate
plans containing explicit conditional statements or loops.
Even though the SRI researchers had grand plans for continuing work on
Shakey, DARPA demurred, and the project ended in 1972. This termination
was unfortunate, because work on planning, vision, learning, and their
integration in robot systems had achieved a great deal of momentum and
enthusiasm among SRI researchers. Furthermore, several new ideas for
planning and visual perception were being investigated. Many of these were
described in detail in a final report for the Shakey project.30
Among these ideas, a particularly important one involved techniques for
constructing plans in a hierarchical fashion. To do so, an overall plan
consisting of just “high-level” actions must be composed first. Such a plan can
be found with much less searching than one consisting of all of the lowest level
actions needed. For example, one’s plan for getting to work might involve only
the decision either to take the subway or to drive one’s car. Then, gradually,
the high-level plan must be refined in more and more detail until actions at
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![12.2 The Stanford Cart
Other planning systems grew out of the NOAH and NONLIN tradition. One
was the interactive plan-generation and plan-execution system SIPE-2
developed by David E. Wilkins at SRI International.35
Another was O-PLAN
developed by Tate and colleagues at the Artificial Intelligence Applications
Institute (AIAI) at the University of Edinburgh.36
These systems have been
widely used, extended, and applied.37
12.2 The Stanford Cart
In the early 1960s, James Adams, a Mechanical Engineering graduate student
at Stanford (and later a Stanford professor), began experimenting with a
four-wheeled, mobile cart with a TV camera and a radio control link. Lester
Earnest wrote (in his history of the several projects using this cart) “Among
other things, Adams showed in his dissertation that with a communication
delay corresponding to the round trip to the Moon (about 21
2 seconds) the
vehicle could not be reliably controlled if traveling faster than about 0.2 mph
(0.3 kph).”38
After Earnest joined the Stanford AI Laboratory, he and Rodney
Schmidt, an Electrical Engineering Ph.D. student, got an upgraded version of
the cart to “follow a high contrast white line [on the road around the Lab]
under controlled lighting conditions at a speed of about 0.8 mph (1.3 kph).”
Other AI graduate students also experimented with the cart from time to time
during the early 1970s. A picture of the cart (as it appeared around this time)
is shown in Fig. 12.8.
When Hans Moravec came to Stanford to pursue Ph.D. studies on visual
navigation, he began work with the cart, “but suffered a setback in October
1973 when the cart toppled off an exit ramp while under manual control and
ended up with battery acid throughout its electronics.” By 1979 Moravec got
the refurbished cart, now equipped with stereo vision, to cross a cluttered
room without human intervention. But it did this very slowly. According to
Moravec,39
The system was reliable for short runs, but slow. The Cart moved
1 m every 10 to 15 min, in lurches. After rolling a meter it stopped,
took some pictures, and thought about them for a long time. Then
it planned a new path, executed a little of it, and paused again. It
successfully drove the Cart through several 20-m courses (each
taking about 5 h) complex enough to necessitate three or four
avoiding swerves; it failed in other trials in revealing ways.
A short video of the cart in action can be seen at
http://www.frc.ri.cmu.edu/users/hpm/talks/Cart.1979/Cart.final.mov. Along
with Shakey, the Stanford Cart resides in the Computer History Museum in
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![12 NOTES
Figure 12.8: The Stanford cart. (Photograph courtesy of Lester Earnest.)
Mountain View, California. They were the progenitors of a long line of robot
vehicles, which will be described in subsequent chapters.
Notes
1. Hans P. Moravec, Robot: Mere Machine to Transcendent Mind, pp. 18–19, Oxford:
Oxford University Press, 1999. [213]
2. A copy of the proposal is available online at http://www.ai.sri.com/pubs/files/1320.pdf.
Its cover page says “Prepared by Nils J. Nilsson,” but it was really a team effort, and many
of the ideas were elaborations of those put forward in Rosen’s 1963 memo. [215]
3. A copy of the complete statement can be found at
http://ai.stanford.edu/∼nilsson/automaton-work-statement.pdf. [215]
4. Online copies of SRI’s proposals for the automaton project, subsequent progress reports,
and related papers can be found at http://www.ai.sri.com/shakey/. [216]
5. See http://www.robothalloffame.org/. [216]
6. See http://www.computerhistory.org/timeline/?category=rai. [216]
232
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![12.2 NOTES
7. J. Doran and Donald Michie, “Experiments with the Graph Traverser Program,”
Proceedings of the Royal Society of London, Series A, Vol. 294, pp. 235–259, 1966. [220]
8. The first written account of this idea was in Charles A. Rosen and Nils Nilsson,
“Application of Intelligent Automata to Reconnaissance,” pp. 21–22, SRI Report, December
1967. Available online at http://www.ai.sri.com/pubs/files/rosen67-p5953-interim3.pdf.
[220]
9. Personal communication, October 24, 2006. [220]
10. See Peter Hart, Nils Nilsson, and Bertram Raphael, “A Formal Basis for the Heuristic
Determination of Minimum Cost Paths,” IEEE Transactions System Science and
Cybernetics, Vol. 4, No. 2, pp. 100–107, 1968, and Peter Hart, Nils Nilsson, and Bertram
Raphael, “Correction to ‘A Formal Basis for the Heuristic Determination of Minimum-Cost
Paths,”’ SIGART Newsletter, No. 37, pp. 28–29, December 1972. [221]
11. See, for example, Korf’s publications at
http://www.cs.ucla.edu/∼korf/publications.html. [221]
12. In a 2003 paper titled “A∗ Parsing: Fast Exact Viterbi Parse Selection,” Dan Klein and
Christopher Manning wrote “The use of A∗ search can dramatically reduce the time
required to find a best parse by conservatively estimating the probabilities of parse
completions.” [221]
13. In an e-mail to Peter Hart dated March 27, 2002, Brian Smart, the chief technical officer
of a vehicle-navigation company, wrote “Like most of the ‘location based services’ and
‘vehicle navigation’ industry, we use a variant of A∗ for computing routes for vehicle and
pedestrian navigation applications.” [221]
14. In an e-mail to me dated June 14, 2003, Steven Woodcock, a consultant on the use of AI
in computer games, wrote “A∗ is far and away the most used. . . and most useful. . . algorithm
for pathfinding in games today. At GDC roundtables since 1999, developers have noted that
they make more use of A∗ than any other tool for pathfinding.” [221]
15. See Bertram Raphael et al., “Research and Applications – Artificial Intelligence,” pp.
27–32, SRI Report, April 1971. Available online at
http://www.ai.sri.com/pubs/files/raphael71-p8973-semi.pdf. [221]
16. For a description of how QA3 developed a plan for Shakey to push three objects to the
same place, for example, see Nils J. Nilsson, “Research on Intelligent Automata,” Stanford
Research Institute Report 7494, pp. 10ff, February 1969; available online at
http://www.ai.sri.com/pubs/files/nilsson69-p7494-interim1.pdf. [222]
17. McCarthy and Hayes first described this problem in John McCarthy and Patrick Hayes,
“Some Philosophical Problems from the Standpoint of Artificial Intelligence,” in Donald
Michie and Bernard Meltzer (eds.), Machine Intelligence, Vol. 4, pp. 463–502, 1969.
Reprinted in Matthew Ginsberg (ed.), Readings in Nonmonotonic Reasoning, pp. 26–45,
San Francisco: Morgan Kaufmann Publishers, Inc., 1987. Preprint available online at
http://www-formal.stanford.edu/jmc/mcchay69/mcchay69.html. [222]
18. See Carl Hewitt, “ PLANNER: A Language for Proving Theorems in Robots,”
Proceedings of the First International Joint Conference on Artificial Intelligence, pp.
295–301, 1969. [223]
19. See Richard Fikes and Nils Nilsson, “STRIPS: A New Approach to the Application of
Theorem Proving to Problem Solving,” Artificial Intelligence, Vol. 2, Nos. 3–4, pp. 189–208,
1971. Available online at
http://ai.stanford.edu/users/nilsson/OnlinePubs-Nils/PublishedPapers/strips.pdf. [223]
20. The generalization and execution mechanisms are described in Richard Fikes, Peter
Hart, and Nils Nilsson, “Learning and Executing Generalized Robot Plans,” Artificial
Intelligence, Vol. 3, No. 4, pp. 251–288, 1972. Available online (as an SRI report) at
http://www.ai.sri.com/pubs/files/tn070-fikes72.pdf. [224]
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![12 NOTES
21. See Claude Brice and Claude Fennema, “Scene Analysis Using Regions,” Artificial
Intelligence, Vol. 1, No. 3, pp. 205–226, 1970. [225]
22. Richard O. Duda and Peter E. Hart, “Use of the Hough Transformation to Detect Lines
and Curves in Pictures,” Communications of the ACM, Vol. 15, pp. 11–15, January 1972.
See also, Peter E. Hart, “How the Hough Transform Was Invented,” IEEE Signal Processing
Magazine, November, 2009. [225]
23. Bertram Raphael et al., “Research and Applications – Artificial Intelligence,” Part V,
SRI Final Report, December 1971; available online at
http://www.ai.sri.com/pubs/files/raphael71-p8973-final.pdf. [226]
24. For more information about Shakey’s visual routines, in addition to the final report just
cited, see Richard O. Duda, “Some Current Techniques for Scene Analysis,” SRI Artificial
Intelligence Group Technical Note 46, October 1970, available online at
http://www.ai.sri.com/pubs/files/tn046-duda70.pdf. [226]
25. This experiment, as well as other information about Shakey, is described in Bertram
Raphael et al., “Research and Applications – Artificial Intelligence,” SRI Final Report,
December 1971, available online at
http://www.ai.sri.com/pubs/files/raphael71-p8973-final.pdf; in Nils Nilsson (ed.), “Shakey
The Robot,” SRI Technical Note 323, April 1984, available online at
http://www.ai.sri.com/pubs/files/629.pdf; and in a 1972 film, Shakey: An Experiment in
Robot Planning and Learning, available online at
http://www.ai.sri.com/movies/Shakey.ram. [228]
26. L. Stephen Coles, “Talking with a Robot in English,” Proceedings of the International
Joint Conference on Artificial Intelligence, Washington, DC, May 7–9, Bedford, MA: The
MITRE Corporation, 1969. [228]
27. See L. Stephen Coles et al., “Application of Intelligent Automata to Reconnaissance,”
SRI Final Report, November 1969, available online at
http://www.ai.sri.com/pubs/files/coles69-p7494-final.pdf. [228]
28. The problem was introduced by McCarthy in his July 1963 memo “Situations, Actions,
and Causal Laws,” reprinted as Section 7.2 of his paper “Program with Commonsense,”
which appeared in Marvin Minsky (ed.), Semantic Information Processing, pp. 403–418,
Cambridge, MA: MIT Press, 1968. [229]
29. E-mail from Eric Horvitz of May 12, 2003. [229]
30. Peter E. Hart et al., “Artificial Intelligence – Research and Applications,” Technical
Report, Stanford Research Institute, December 1972. (Available online at
http://www.ai.sri.com/pubs/files/hart72-p1530-annual.pdf.) See also Richard E. Fikes,
Peter E. Hart, and Nils J. Nilsson, “Some New Directions in Robot Problem Solving,” in
Machine Intelligence 7, Bernard Meltzer and Donald Michie (eds.), pp. 405–430, Edinburgh:
Edinburgh University Press, 1972. (The SRI Technical Note 68 version is available online at
http://www.ai.sri.com/pubs/files/1484.pdf.) [229]
31. Earl D. Sacerdoti, “Planning in a Hierarchy of Abstraction Spaces,” pp. 412–422,
Proceedings of the Third International Joint Conference on Artificial Intelligence, 1973.
(The SRI AI Center Technical Note 78 version is available online at
http://www.ai.sri.com/pubs/files/1501.pdf.) [230]
32. Earl D. Sacerdoti, “The Non-Linear Nature of Plans,” Proceedings of the International
Joint Conference on Artificial Intelligence, 1975. (The SRI AI Center Technical Note 101
version is available online at http://www.ai.sri.com/pubs/files/1385.pdf.) Also see Earl D.
Sacerdoti, A Structure for Plans and Behavior, New York: Elsevier North-Holland, 1977.
(The SRI AI Center Technical Note No. 109 version is available online at
http://www.ai.sri.com/pubs/files/762.pdf.) [230]
33. Austin Tate, “Interacting Goals and Their Use,” Proceedings of the Fourth International
Joint Conference on Artificial Intelligence (IJCAI-75), pp. 215–218, Tbilisi, USSR,
234
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![12.2 NOTES
September 1975; available online at http://www.aiai.ed.ac.uk/project/oplan/documents/
1990-PRE/1975-ijcai-tate-interacting-goals.pdf. Austin Tate, “Using Goal Structure to
Direct Search in a Problem Solver,” Ph.D. thesis, University of Edinburgh, September 1975;
available online at http://www.aiai.ed.ac.uk/project/oplan/documents/1990-PRE/. [230]
34. Austin Tate, “Generating Project Networks,” Proceedings of the Fifth International
Joint Conference on Artificial Intelligence (IJCAI-77) pp. 888–893, Boston, MA, August
1977; available online at http://www.aiai.ed.ac.uk/project/oplan/documents/1990-PRE/
1977-ijcai-tate-generating-project-networks.pdf. [230]
35. See the SIPE-2 Web page at http://www.ai.sri.com/∼sipe/. [231]
36. Ken Currie and Austin Tate, “O-PLAN: The Open Planning Architecture,” Artificial
Intelligence, Vol. 52, pp. 49–86, 1991. Available online at
http://www.aiai.ed.ac.uk/project/oplan/documents/1991/91-aij-oplan-as-published.pdf.
[231]
37. See, for example, AIAI’s “Planning and Activity Management” Web page at
http://www.aiai.ed.ac.uk/project/plan/. [231]
38. Lester Earnest, “Stanford Cart,” August 2005; available online at
http://www.stanford.edu/∼learnest/cart.htm. [231]
39. Hans P. Moravec, “The Stanford Cart and the CMU Rover,” Proceedings of the IEEE,
Vol. 71, No. 7, pp. 872–884, July 1983. [231]
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![13 Progress in Natural Language Processing
system. It was tested in early 1969 at the Wright-Patterson Air Force Base in
Dayton, Ohio, “where it continues to provide Russian–English translations for
the USAF’s Foreign Technology Division to this day.”3
Systran has evolved to
be one of the main automatic translation systems. It is marketed by the
Imageforce Corporation in Tampa, Florida.4
How well does Systran translate? It all depends on how one wants to
measure performance. Margaret Boden mentions two measures, namely,
“intelligibility” and “correctness.” Both of these measures depend on human
judgement. For the first, one asks “Can the translation be generally
understood?” For the second, one asks “Do human ‘post-editors’ need to
modify the translation?” Boden states that “in the two-year period from 1976
to 1978, the intelligibility of translations generated by Systran rose from 45 to
78 percent for [raw text input]. . . ” She also notes that human translations
score only 98 to 99 percent, not 100 percent. Regarding correctness, Boden
states that in 1978 “only 64 percent of the words were left untouched by
human post-editors. Even so, human post-editing of a page of Systran output
took only twenty minutes in the mid-1980s, whereas normal (fully human)
translation would have taken an hour.”5
13.2 Understanding
Although the late 1960s and early 1970s might have been a “quiet decade” for
machine translation, it was a very active period for other NLP work.
Researchers during these years applied much more powerful syntactic,
semantic, and inference abilities to the problem of understanding natural
language. Typical of the new attitude was the following observation by Terry
Winograd, an MIT Ph.D. student during the late 1960s:6
If we really want computers to understand us, we need to give
them ability to use more knowledge. In addition to a grammar of
the language, they need to have all sorts of knowledge about the
subject they are discussing, and they have to use reasoning to
combine facts in the right way to understand a sentence and to
respond to it. The process of understanding a sentence has to
combine grammar, semantics, and reasoning in a very intimate
way, calling on each part to help with the others.
13.2.1 SHRDLU
Perhaps the NLP achievement that caused the greatest excitement was the
SHRDLU natural language dialog system programmed by Terry Winograd
(1946– ; Fig. 13.1) for his Ph.D. dissertation (under Seymour Papert) at MIT.7
238
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![13.2 Understanding
GUS: In what name should I make a reservation, please?
(5) Client: Dan Bobrow.
GUS: I have confirmed the following flight: P.SA. flight 102 on
Wednesday May 28 from San Jose to San Diego that leaves at 7.30
am and arrives at 9.15 am. What date do you want to return on?
(6) Client: On Friday in the evening.
GUS: Would you like the flight that leaves at 7.45 pm?
(7) Client: That’s fine.
GUS: I have confirmed the following flight: P.S.A. flight 307 on
Friday May 30 from San Diego to San Jose that leaves at 7.45 pm
and arrives at 9.30 pm. Thank you for calling. Goodbye.
Although the abilities of the system were certainly limited, GUS was able
to deal with a number of problems. One of these involves what NLP
researchers call “resolving anaphora,” by which they mean deciding on the
objects or events to which various words or phrases in a dialog refer. Several
examples, keyed to the numbered sentences in the dialog above, are mentioned
in the paper about GUS:
At line (3), for example, the client’s query refers to the flight
mentioned in GUS’s immediately preceding utterance. In (4) there
is a reference to the flight mentioned earlier in the conversation,
[following line (2)]. Note that “next flight” in (3) was to be
interpreted relative to the order of flights in the airline guide
whereas “first one” in (4) refers to the order in which the flights
were mentioned. Another implicit referent underlies the use of
“Friday” to specify a date in (6). Resolution of this reference
requires some complicated reasoning involving both the content
and the context of the conversation. Since May 28 has been given
as the departure date, it must presumably be the following Friday
that the client has in mind. On the other hand, suppose that the
specifications were reversed and Friday had been given as the
departure date at line (1). It would then be most readily
interpretable as referring to the Friday immediately following the
conversation.
GUS was a combination of several communicating subsystems, a
morphological analyzer for dealing with word components, a syntactic analyzer
for generating parse trees, a “reasoner” for figuring out a user’s meanings and
intentions, and a language generator for responding. Controlling these
components was done by using an “agenda” mechanism. As the authors
explain,
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![13 Progress in Natural Language Processing
GUS operates in a cycle in which it examines this agenda, chooses
the next job to be done, and does it. In general, the execution of
the selected task causes entries for new tasks to be created and
placed on the agenda. Output text generation can be prompted by
reasoning processes at any time, and inputs from the client are
handled whenever they come in. There are places at which
information from a later stage (such as one involving semantics)
are fed back to an earlier stage (such as the parser). A supervisory
process can reorder the agenda at any time.
The syntactic component of GUS had “access to a main dictionary of more
than 3,000 stems and simple idioms.” The syntactic analyzer was based on a
system developed earlier by Ronald Kaplan, which used a transition-network
grammar and was called a “General Syntactic Processor.”23
Client sentences
were encoded in “frames” (which are related to Minsky’s frames but closer in
form to semantic networks). Some frames described the sequence of a normal
dialog, whereas others represented the attributes of a date, a trip plan, or a
traveler. GUS’s reasoning component used the content and structure of the
frames to deduce how best to interpret client sentences.
Besides anaphora, the paper mentioned several other problems that GUS
was able to deal with. However, it also cautioned that “it is much too easy to
extrapolate from [the sample dialog] a mistaken notion that GUS contained
solutions to far more problems than it did.” Sample dialogs recorded between
human clients and humans playing the role of a GUS revealed numerous
instances in which the computer GUS would fail. The authors concluded that if
users of systems like GUS departed “from the behavior expected of them in the
minutest detail, or if apparently insignificant adjustments are made in their
structure,” the systems would act as if they had “gross aphasia” or had just
simply died. The authors conceded that “GUS itself is not very intelligent, but
it does illustrate what we believe to be essential components of [an intelligent
language understanding] system. . . . [It] must have a high quality parser, a
reasoning component, and a well structured data base of knowledge.”
Subsequent work on NLP at PARC and many other places sought to improve
all of these components.
The systems developed by researchers such as Winograd, Woods, Bobrow,
and their colleagues were very impressive steps toward conversing with
computers in English. Yet, there was still a long way to go before natural
language understanding systems could perform in a way envisioned by
Winograd in the preface to his Ph.D. dissertation:
Let us envision a new way of using computers so they can take
instructions in a way suited to their jobs. We will talk to them just
as we talk to a research assistant, librarian, or secretary, and they
will carry out our commands and provide us with the information
we ask for. If our instructions aren’t clear enough, they will ask for
248
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![13.2 NOTES
more information before they do what we want, and this dialog will
all be in English.
Notes
1. W. John Hutchins, “Machine Translation: A Brief History,” in E. F. K. Koerner and R.
E. Asher (eds.), Concise History of the Language Sciences: From the Sumerians to the
Cognitivists, pp. 431–445, Oxford: Pergamon Press, 1995. (Also available online at
http://ourworld.compuserve.com/homepages/WJHutchins/Conchist.htm.) [237]
2. Ibid. [237]
3. W. John Hutchins, Machine Translation: Past, Present, Future, Chichester: Ellis
Horwood, 1986. An updated (2003) version is available online at
http://www.hutchinsweb.me.uk/PPF-TOC.htm. Some of the technical details of Systran’s
operation are described in the book. [238]
4. http://www.translationsoftware4u.com/. [238]
5. Margaret A. Boden, Mind as Machine: A History of Cognitive Science, p. 683, Oxford:
Oxford University Press, 2006. [238]
6. This quote is from the preface of Winograd’s Ph.D. dissertation. [238]
7. SHRDLU is described in Winograd’s dissertation “Procedures as a Representation for
Data in a Computer Program for Understanding Natural Language.” It was issued as an
MIT AI Technical Report No. 235, February 1971, and is available online at
https://dspace.mit.edu/bitstream/1721.1/7095/2/AITR-235.pdf. The thesis was also
published as a full issue of Cognitive Psychology, Vol. 3, No. 1, 1972, and as a book
Understanding Natural Language, New York: Academic Press, 1972. The letters in
SHRDLU comprise the second column of keys in linotype machines, which were used to set
type before computers were used for that. This nonsense word was often used in MAD
magazine, which Winograd read in his youth. Failing to think of an acceptable acronym to
use to name his system, Winograd used SHRDLU. For Winograd’s account, see
http://hci.stanford.edu/∼winograd/shrdlu/name.html. [238]
8. Taken from Section 1.3 of Winograd’s thesis. [239]
9. Readers interested in the entire dialog can see it either in Winograd’s thesis or on one of
his Web sites at http://hci.stanford.edu/∼winograd/shrdlu/. [241]
10. Winograd cites, among others, M. A. K. Halliday, “Categories of the Theory of
Grammar,” Word, Vol, 17, No. 3, pp. 241-292, 1961. [241]
11. For a short film of SHRDLU in action, see
http://projects.csail.mit.edu/films/aifilms/digitalFilms/3mpeg/26-robot.mpg. [242]
12. From http://www.semaphorecorp.com/misc/shrdlu.html. [242]
13. William A. Woods, “Semantics for a Question-Answering System,” Ph.D. dissertation,
Harvard University, August 1967. Reprinted as a volume in the series Outstanding
Dissertations in the Computer Sciences, New York: Garland Publishing, 1979. [243]
14. William A. Woods, Ron M. Kaplan, and Bonnie Nash-Webber, “The Lunar Sciences
Natural Language Information System: Final Report,” BBN, Cambridge, MA, June 1, 1972.
See also William A. Woods, “Progress in Natural Language Understanding – An Application
to Lunar Geology,” AFIPS Conference Proceedings, Vol. 42, pp. 441–450, Montvale, New
Jersey: AFIPS Press, 1973. [243]
15. See http://www.ils.albany.edu/IQA06/Files/Bill Woods IQA06.pdf. [243]
Copyright c 2010 Nils J. Nilsson
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![13 NOTES
16. William A. Woods, “Progress in Natural Language Understanding – An Application to
Lunar Geology,” AFIPS Conference Proceedings, Vol. 42, pp. 441–450, Montvale, New
Jersey: AFIPS Press, 1973. [244]
17. Noam Chomsky, Syntactic Structures, ’s-Gravenhage: Mouton & Co., 1957. [244]
18. James Thorne, Paul Bratley, and Hamish Dewar, “The Syntactic Analysis of English by
Machine,” in D. Michie (ed.), Machine Intelligence 3, pp. 281–309, New York: American
Elsevier Publishing Co., 1968; Hamish Dewar, Paul Bratley, and James Thorne, “A Program
for the Syntactic Analysis of English Sentences,” Communications of the ACM, Vol. 12, No.
8, pp. 476–479, August 1969. [245]
19. Daniel Bobrow and Bruce Fraser, “An Augmented State Transition Network Analysis
Procedure,” Proceedings of the International Joint Conferenece on Artificial Intelligence,
pp. 557–567, Washington, DC, 1969. [245]
20. William A. Woods, “Augmented Transition Networks for Natural Language Analysis,”
Report CS-1, Aiken Computation Laboratory, Harvard University, Cambridge, MA,
December 1969; William A. Woods, “Transition Network Grammars for Natural Language
Analysis,” Communications of the ACM, Vol. 13, No. 10, pp. 591–606, 1970. The ACM
article has been reprinted in Yoh-Han Pao and George W. Ernest (eds.), Tutorial:
Context-Directed Pattern Recognition and Machine Intelligence Techniques for Information
Processing, Silver Spring, MD: IEEE Computer Society Press, 1982, and in Barbara Grosz,
Karen Sparck Jones, and Bonnie Webber (eds.), Readings in Natural Language Processing,
San Mateo, CA: Morgan Kaufmann, 1986. [245]
21. William A. Woods, op. cit., pp. 602ff. [245]
22. Daniel G. Bobrow et al., “GUS, A Frame-Driven Dialog System,” Artificial Intelligence,
Vol. 8, pp. 155–173, 1977. [246]
23. Ronald Kaplan, “A General Syntactic Processor,” in R. Rustin (ed.), Natural Language
Processing, New York: Algorithmics Press, 1973. [248]
250
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![14.0 NOTES
level on the U.S. Chess Federation rating scale.” Subsequent Northwestern
programs, up through CHESS 4.6, achieved strings of wins at this annual event.
Meanwhile, however, CHESS 4.2 was beaten in an early round of the first World
Computer Chess Championship tournament held at the International
Federation of Information Processing Societies (IFIPS) meeting in Stockholm
in 1974. The Russian program, Kaissa, won all four games in that tournament,
thereby becoming the world computer chess champion.11
These years, the late 1960s through the mid-1970s, saw computer chess
programs gradually improving from beginner-level play to middle-level play.
Work on computer chess during the next two decades would ultimately achieve
expert-level play, as we shall see in a subsequent chapter. Despite this rapid
progress, it was already becoming apparent that there was a great difference
between how computers played chess and how humans played chess. As Hans
Berliner, a chess expert and a chess programming expert, put it in an article in
Nature,12
[A human] uses prodigious amounts of knowledge in the
pattern-recognition process [to decide on a good maneuver] and a
small amount of calculation to verify the fact that the proposed
solution is good in the present instance. . . . However, the computer
would make the same maneuver because it found at the end of a
very large search that it was the most advantageous way to
proceed out of the hundreds of thousands of possibilities it looked
at. CHESS 4.6 has to date made several well known maneuvers
without having the slightest knowledge of the maneuver, the
conditions for its applications, and so on; but only knowing that
the end result of the maneuver was good.
Berliner summed up the difference by saying that “The basis of human
chess strength, by contrast [with computers], is accumulated knowledge” (my
italics). Specific knowledge about the problem being solved, as opposed to the
use of massive search in solving the problem, came to be a major theme of
artificial intelligence research during this period. (Later, however, massive
search regained some of its importance.) Perhaps the most influential
proponents of the use of knowledge in problem solving were Edward
Feigenbaum and his colleagues at Stanford. I’ll turn next to their seminal
work.
Notes
1. Arthur L. Samuel, “Some Studies in Machine Learning Using the Game of Checkers II –
Recent Progress,” IBM Journal of Research and Development, Vol. 11, No. 6, pp. 601–617,
1967. [251]
2. http://www.cs.ualberta.ca/∼sutton/book/11/node3.html. [251]
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![14 NOTES
3. Alan Kotok, “A Chess Playing Program for the IBM 7090 Computer” MIT bachelor’s
thesis in Electrical Engineering, June 1962. Online versions of the thesis are available at
http://www.kotok.org/AK-Thesis-1962.pdf and http://www.kotok.org/AI Memo 41.html.
(The latter is an MIT memo in which Kotok pointed out that “. . . this report, while written
by me, represents joint work of ‘the chess group,’ which consisted of me, Elwyn R.
Berlekamp (for the first year), Michael Lieberman, Charles Niessen, and Robert A. Wagner
(for the third year). We are all members of the MIT [undergraduate] Class of 1962.) [251]
4. The Computer History Museum has a video “oral history” of Kotok available at
http://www.computerhistory.org/chess/alan kotok.oral history highlight.102645440/index.
php?iid=orl-433444ecc827d. [251]
5. G. M. Adelson-Velskiy, V. L. Arlazarov, A. R. Bitman, A. A. Zhivotovskii and A.V.
Uskov, “Programming a Computer to Play Chess,” Russian Mathematical Surveys 25,
March–April 1970, pp. 221–262, London: Cleaver-Hume Press. (Translation of Proceedings
of the 1st Summer School on Mathematical Programming, Vol. 2, pp. 216–252, 1969.) [251]
6. See the oral presentation about the history of computer chess at
http://video.google.com/videoplay?docid=-1583888480148765375. [252]
7. Richard D. Greenblatt, Donald E. Eastlake III, and Stephen D. Crocker, “The
Greenblatt Chess Program,” AI Memo 174, April 1969. Available online at
https://dspace.mit.edu/bitstream/1721.1/6176/2/AIM-174.pdf. [252]
8. See an account in the SIGART Newsletter, December 1968. [252]
9. Hubert L. Dreyfus, “Alchemy and Artificial Intelligence,” RAND paper P-3244, p. 10,
The RAND Corporation, Santa Monica, CA, December 1965. Available online at
http://www.rand.org/pubs/papers/2006/P3244.pdf. [252]
10. For first-hand details about the bet, see David Levy, Robots Unlimited: Life in a Virtual
Age, p. 83, Wellesley, MA: A K Peters, Ltd., 2006. [252]
11. See the Computer History Museum’s exhibits on the history of computer chess at
http://www.computerhistory.org/chess/index.php. For a concise timeline of computer chess
history compiled by Bill Wall, visit
http://www.geocities.com/SiliconValley/Lab/7378/comphis.htm. [253]
12. Hans J. Berliner, “Computer Chess,” Nature, Vol. 274, p. 747, August 1978. [253]
254
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![15.0 NOTES
power of the inference method employed. Our agents must be
knowledge-rich, even if they are methods-poor.
Embedding the knowledge of experts in AI programs led to the development of
many “expert systems,” as we shall see later. It also led to increased
concentration on specific and highly constrained problems and away from
focusing on the general mechanisms of intelligence, whatever they might be.
Notes
1. The quotation taken from “Comments by Edward A. Feigenbaum” in Edward H.
Shortliffe and Thomas C. Rindfleisch, “Presentation of the Morris F. Collen Award to
Joshua Lederberg,” Journal of the American Medical Informatics Association, Vol. 7, No.
3, pp. 326–332, May–June 2000. Available online at
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=61437. [255]
2. For an interesting account of the history of their collaboration, see “How DENDRAL
Was Conceived and Born,” by Joshua Lederberg, a paper presented at the Association for
Computing Machinery (ACM) Symposium on the History of Medical Informatics at the
National Library of Medicine on November 5, 1987. Later published in Bruce I. Blum and
Karen Duncan (eds.), A History of Medical Informatics, pp. 14–44, New York: Association
for Computing Machinery Press, 1990. Typescript available online at
http://profiles.nlm.nih.gov/BB/A/L/Y/P/ /bbalyp.pdf. [255]
3. Robert K. Lindsay, Bruce G. Buchanan, Edward A. Feigenbaum, and Joshua Lederberg,
Applications of Artificial Intelligence for Organic Chemistry: The Dendral Project, p. 70,
New York: McGraw-Hill Book Co., 1980. [258]
4. For a thorough account of achievements of the Dendral project, see ibid. [258]
5. The hypothesis seems to have been implicit in Edward A. Feigenbaum, “Artificial
Intelligence: Themes in the Second Decade,” Supplement to Proceedings of the IFIP 68
International Congress, Edinburgh, August 1968. Published in A. J. H. Morrell (ed.),
Information Processing 68, Vol. II, pp. 1008–1022, Amsterdam: North-Holland, 1969. [258]
6. Edward A. Feigenbaum, “The Art of Artificial Intelligence: Themes and Case Studies of
Knowledge Engineering,” Proceedings of the Fifth International Joint Conference on
Artificial Intelligence, pp. 1014–1029, 1977. See also Edward A. Feigenbaum, “The Art of
Artificial Intelligence: I. Themes and Case Studies of Knowledge Engineering,” Stanford
Heuristic Programming Project Memo HPP-77-25, August 1977, which is available online at
http://infolab.stanford.edu/pub/cstr/reports/cs/tr/77/621/CS-TR-77-621.pdf. [258]
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![16.0
workshops were held every few years at various venues. Each workshop
resulted in a book with the title Machine Intelligence N, where N denotes the
workshop and volume number.9
The last few volumes have been published
online by the Electronic Transactions on Artificial Intelligence.10
These books
contain some of the most cited and important papers in the early history of
the field.
These years saw the United States engaged in war in Vietnam, and
Congress wanted to make sure that research supported by the U.S. Defense
Department was relevant to military needs. Responding to these pressures, on
November 19, 1969, Congress passed the “Mansfield Amendment” to the
Defense Procurement Authorization Act of 1970 (Public Law 91-121), which
required that the Defense Department restrict its support of basic research to
projects “with a direct and apparent relationship to a specific military
function or operation.” On March 23, 1972, the Advanced Research Projects
Agency was renamed the Defense Research Advanced Projects Agency
(DARPA) to reflect its emphasis on projects that contributed to enhanced
military capabilities. (The name reverted to the Advanced Research Projects
Agency in 1993 and then back to the Defense Advanced Research Projects
Agency in 1996.)11
On the other side of the Atlantic, British AI researchers experienced their
own funding crisis. One of the U.K.’s main funding bodies for university
research, the Science Research Council, asked Professor James Lighthill, a
famous hydrodynamicist at Cambridge University, to undertake an evaluative
study of artificial intelligence research. Lighthill’s report, titled “Artificial
Intelligence: A General Survey,” somewhat idiosyncratically divided AI
research into three categories, namely, advanced automation, computer-based
studies of the central nervous system, and the bridges in between. He called
these categories A, C, and B, respectively. Although he came out in favor of
continued work in categories A and C, he was quite critical of most AI basic
research, including robotics and language processing, which he lumped into
category B. He wrote that “In no part of the field have the discoveries made so
far produced the major impact that was then [around 1960] promised.” He
concluded that AI’s existing search techniques (which worked on toy
problems) would not scale to real problems because they would be stymied by
the combinatorial explosion.12
Lighthill’s report resulted in a substantial curtailment of AI research in
the United Kingdom. In particular, one of its casualties was work on
FREDDY the robot and other AI work under Donald Michie at Edinburgh.
Here is one of Michie’s later comments about the effects of the report:13
Work of excellence by talented young people was stigmatised as
bad science and the experiment killed in mid-trajectory. This
destruction of a co-operative human mechanism and of the careful
craft of many hands is elsewhere described as a mishap. But to
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![16 NOTES
speak plainly, it was an outrage. In some later time, when the
values and methods of science have further expanded, and those of
adversary politics have contracted, it will be seen as such.
DARPA’s shift to shorter term applied research, together with the
Lighthill report and criticisms from various onlookers, posed difficulties for
basic AI research during the next few years. Nevertheless, counter to
Lighthill’s assessment, many AI techniques did begin to find application to
real problems, launching a period of expansion in AI applications work, as
we’ll see in the next few chapters.
Notes
1. See http://www.ijcai.org/IJCAItrustees.php. [261]
2. Edward A. Feigenbaum and Julian Feldman, Computers and Thought, New York:
McGraw-Hill Book Co., 1963. (The McGraw-Hill volume is now out of print; it is now
available through AAAI Press/MIT Press, 1995 edition.) [262]
3. Nils J. Nilsson, Learning Machines: Foundations of Trainable Pattern-Classifying
Systems, New York: McGraw-Hill Book Co., 1965; republished as The Mathematical
Foundations of Learning Machines, San Francisco: Morgan Kaufmann Publishers, 1990.
[262]
4. Nils J. Nilsson, Problem-Solving Methods in Artificial Intelligence, New York:
McGraw-Hill Book Co., 1971. [262]
5. James R. Slagle, Artificial Intelligence: The Heuristic Programming Approach, New
York: McGraw-Hill Book Co., 1971. [262]
6. Bertram Raphael, The Thinking Computer: Mind Inside Matter, New York: W. H.
Freeman, 1976. [262]
7. Marvin Minsky and Seymour Papert, Perceptrons: An Introduction to Computational
Geometry, Cambridge, MA: MIT Press, 1969. [262]
8. See Frank Rosenblatt, J. T. Farrow, and S. Rhine, “The Transfer of Learned Behavior
from Trained to Untrained Rats by Means of Brain Extracts. I,” Proceedings of the National
Academy of Sciences, Vol. 55, No. 3, pp. 548–555, March 1966. [262]
9. The series maintains a Web page at http://www.cs.york.ac.uk/mlg/MI/mi.html. [263]
10. See http://www.etai.info/mi/. [263]
11. See http://www.darpa.mil/body/arpa darpa.html. [263]
12. The text of the report, along with commentary and criticism by leading British AI
researchers, was published in 1972 in James Lighthill et al. (eds.), Artificial Intelligence: A
Paper Symposium, London: Science Research Council of Great Britain, 1972. [263]
13. Donald Michie, Machine Intelligence and Related Topics: An Information Scientist’s
Weekend Book, p. 220, New York: Gordon and Breach Science Publishers, 1982. [263]
264
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![17.2 Speech Processing
Figure 17.2: Consonants and vowels in the ARPAbet phonetic alphabet.
“you” might be pronounced differently in “are you” [aa r y uw] and “did you”
[d ih d jh uh].
Attempts to recognize speech began at Bell Laboratories as far back as
the 1930s. In 1952, engineers at Bell Labs built a system for recognizing the
numbers “zero” through “nine” uttered by a single speaker.1
Other work was
done in the 1950s and 1960s at RCA Laboratories, at MIT, in Japan, in
England, and in the Soviet Union.2
Work accelerated in the 1970s, some of
which I’ll describe next.
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![17.3 The DARPA Speech Understanding Research Program
optimistic about success. One naysayer was John R. Pierce, a researcher at
Bell Laboratories, where much speech-recognition work had already taken
place. In 1969, Pierce wrote a letter6
to the Journal of the Acoustical Society
of America in which he claimed that most people working on speech
recognition were acting like “mad scientists and untrustworthy engineers. The
typical recognizer gets it into his head that he can solve ‘the problem.’” In the
same letter, though, he also wrote that
. . . performance would continue to be very limited unless the
recognizing device understands what is being said with something
of the facility of a native speaker (that is, better than a foreigner
who is fluent in the language). If this is so, should people continue
work toward speech recognition? Perhaps this is for people in the
field to decide. [My italics.]
17.3 The DARPA Speech Understanding
Research Program
In fact, people in the field did decide. In October 1971, Roberts established at
DARPA a five-year Speech Understanding Research (SUR) program based
largely on the study group’s report. Its budget was about $3 million per year.
CMU, Lincoln Laboratory, BBN, SDC, and SRI were contracted to build
systems. Complementary research would be performed at Haskins
Laboratories, the Speech Communications Research Laboratory, the Sperry
Univac Speech Communications Department, and the University of California
at Berkeley.
In 1976 some of these efforts resulted in systems that were demonstrated
and tested against the program’s goals. CMU developed two of these, HARPY
and HEARSAY-II. BBN produced HWIM (Hear What I Mean). SRI and SDC
formed a partnership in which SDC developed the acoustic processing
components and SRI developed the parsing and semantic components.
However, the SDC effort ran into difficulties with computer access, so the
combined SRI/SDC system was never formally tested. I’ll briefly summarize
the BBN work and then describe the CMU work in more detail.7
17.3.1 Work at BBN
SPEECHLIS was the first speech understanding system developed at BBN. It
was designed to answer spoken questions about the moon rocks database (the
one used in BBN’s earlier LUNAR system). It was rather slow and was not
systematically tested.8
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![17 Speech Recognition and Understanding Systems
3. Select that x, say X, for which the product is the largest. [We can ignore
dividing by p(y) because its value does not affect which p(x | Y ) is
largest.]
Although this process would work in principle, it is quite impractical
computationally. Instead, DRAGON and other modern speech-recognition
systems exploit the hierarchical structure involved in what is presumed to be
the way a speech waveform is generated. There are various levels in this
hierarchy that could be identified. To oversimplify a bit, at the top of the
hierarchy a given semantic idea is expressed by a string of words obeying the
syntactic rules of the language. The string of words, in turn, gives rise to a
string of phones – the phonetic units. Finally, the phone string is expressed by
a speech waveform at the bottom of the hierarchy.
At each level, we have a sequence of entities, say, x1, x2, . . . xn, producing
a sequence of other entities, say, y1, y2, . . . , yn. We can diagram the process as
shown in Fig. 17.3.
Figure 17.3: Two hierarchical levels in speech generation.
The DRAGON system made some simplifying assumptions. It assumed
that each xi in the sequence of x’s is influenced only by its immediate
precedent, xi−1, and not by any other of the xi. This assumption is called the
Markov assumption. [Andrey Andreyevich Markov (1856–1922) was a Russian
mathematician. He used (what was later called) a Markov model to analyze
the statistics of a sequence of 20,000 Russian letters taken from Pushkin’s
274
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![17.3 The DARPA Speech Understanding Research Program
novel Eugene Onegin.14
Markov models are used extensively in physics and
engineering. Google uses the Markov assumption, for example, in its
computation of page rank.] Of course, we know that each word in a sequence
depends on more than just the immediately preceding word. Even so, the
Markov assumption makes computations simpler and still allows good
performance.
Further, it was assumed that each yi was influenced only by xi and xi−1.
All of these “influences” are probabilistic. That is, given quantities like x3 and
x4, for example, the value of y4 is not completely determined. One can only
say what the probabilities of the values of y4 might be; these are given by the
functional expression p(y4 | x3, x4). Probability values for the y’s are thus
given by what is called a “probabilistic function of a Markov process.” To
produce estimates of these probabilities, statistics can be gathered during a
“learning process” (in which a speaker utters a training set of sentences).
DRAGON combined these separate levels into a network consisting of a
hierarchy of probabilistic functions of Markov processes. Entities representing
segments of the speech waveform were at the bottom, entities representing
phones were in the middle, and entities representing words were at the top. At
each level, Bayes’s rule was used to compute probabilities of the x’s given the
y’s. Because only the speech waveform at the bottom level was actually
observed, the phones and words were said to be “hidden.” For this reason, the
entire network employed hidden Markov models (HMMs). DRAGON was the
first example of the use of HMMs in AI. They had been developed previously
for other purposes.15
Using this network, recognition of an utterance was then achieved by
finding the highest probability path through the network. Computing the
probabilities for syntactically valid word sequences, given the sequence of
segments of the observed speech waveform, is a problem that is similar to one
I described earlier, namely, computing the confidences of strings of characters
on FORTRAN coding sheets (see p. 101). Again, a method based on dynamic
programming was used. As Baker wrote, “The optimum path is found by an
algorithm which, in effect, explores all possible paths in parallel.”16
At the end
of the process, the most probable syntactically legal string of words is
identified. The mathematical operations for making these computations are
too complex to explain here, but they can be performed efficiently enough to
make speech recognition practical.
Although the DRAGON system was not among those that were finally
tested against DARPA’s speech understanding system objectives, Baker
claimed that its initial results were “very promising” and that in “its first test
with live speech input, the system correctly recognized every word in all nine
sentences in the test.”17
DRAGON became the basis for a commercial product,
“Dragon Naturally Speaking,” first developed and marketed by Dragon
Systems, a company founded by Baker and his wife, Janet.
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![17 Speech Recognition and Understanding Systems
knowledge sources were doing their reading and writing. There were some
knowledge sources that could write predictions about new words based on
words already written in the word layer and on information in other layers.
Knowledge sources could even write guesses about words in the word layer
based on word sequences already written (with high confidence) in the
sequence layer. This process of inferring what must be present in a lower layer
(even though missed by initial processing) from what (from other evidence) is
present in a higher layer is a theme that recurs often in later AI research. As
far as I know, this extremely important AI innovation was first manifest in the
HEARSAY-II system.
According to Raj Reddy,22
one of the inventors of the Blackboard
architecture (along with Victor Lesser, Lee Erman, and Frederick
Hayes-Roth), Herbert Simon often used the word “blackboard” to describe the
“working memory” component of the production system architecture he and
Allen Newell were working with (see p. 577). A production system used
IF–THEN rules (called productions), which were triggered by contents of the
working memory and wrote new data in it. Reddy and team, recognizing the
variety of different sources of knowledge relevant to speech processing,
generalized the production system idea, extending the production rules into
larger programs, renaming them “knowledge sources,” and elaborated working
memory into the layered Blackboard structure.
At the end of the DARPA speech understanding project, HEARSAY-II was
tested on twenty-three spoken sentences, brand new to the system, having an
average of seven words per sentence, and 81% of these were recognized
word-for-word correctly, although 91% led to the same database query as
would have a word-for-word correct sentence. HEARSAY’s designers claimed
that this performance “comes close to meeting the ambitious
goals. . . established for the DARPA program in 1971.” Although HEARSAY-II
came close the results were not quite as good as those of HARPY.
Although the Blackboard architecture is no longer used in modern speech
recognition systems, it was adopted by several other AI programs. (We’ll see
one of these later in the book.) According to Russell and Norvig, “Blackboard
systems are the foundation of modern user interface architectures.”23
17.3.3 Summary and Impact of the SUR Program
CMU’s HEARSAY-II and HARPY were demonstrated at CMU on September 8,
1976, and BBN’s HWIM was demonstrated at BBN on September 10. In a
summary report of the projects, MIT’s Dennis Klatt wrote that “it is unclear
whether there are large differences in ability among [these] three systems.
However, only [HARPY] was able to meet the ARPA goals.”24
The developers of HEARSAY-II attributed HARPY’s superior performance
to three factors: its more thorough search of potential solutions (permitted by
280
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![17.4 Subsequent Work in Speech Recognition
its precomputed network of all the sentences that might have been spoken), its
more thorough built-in knowledge of transition phenomena between adjacent
words, and its more thorough testing, tuning, and debugging.25
Some researchers and DARPA program managers, however, argued about
the way in which the tests were carried out and claimed that none of the
systems met the SUR program objectives. In any case, DARPA decided not to
fund a proposed follow-on program. The program did show, however, that
speech understanding was a reasonable technical goal and stimulated progress
in speech processing technologies, notably in system organization, syntax and
semantics, and acoustic processing. A National Research Council report
concluded that “DARPA’s funding of research on understanding speech has
been extremely important. . . . the results of this research have been
incorporated into the products of established companies, such as IBM and
BBN, as well as start-ups such as Nuance Communications (an SRI spinoff)
and Dragon Systems. . . . The leading commercial speech-recognition program
on the market today, the Dragon “NaturallySpeaking” software [now sold by
Nuance], traces its roots directly back to the work done at CMU between 1971
and 1975 as part of SUR. . . .”26
17.4 Subsequent Work in Speech Recognition
Speech recognition research was also being carried out in other laboratories
besides those that were directly involved with DARPA’s SUR program. For
example, Frederick Jelinek of the Speech Processing Group in IBM’s
Computer Sciences Department at the Thomas J. Watson Research Center in
Yorktown Heights, New York, is credited with being an early proponent of the
use of statistical methods (including hidden Markov models) in speech
recognition.27
The HMM approach was ultimately adopted by all the leading
speech recognition companies.
In 1984, DARPA began funding speech recognition work again as part of
its “Strategic Computing” program (a program that will be described in a
later chapter). Participants included CMU, SRI, BBN, MIT, IBM, and
Dragon Systems. Among the systems developed at CMU over the next several
years, for example, were SPHINX by Kai-Fu Lee and others and JANUS, a
multilingual speech recognition and translation system, by Alex Waibel and
others. (These and other systems are available as open-source software from
the “Speech at CMU” Web page, http://www.speech.cs.cmu.edu/. The page
also has links to many other speech recognition laboratories.)
Based on their work on DRAGON at CMU, James and Janet Baker
founded Dragon Systems in 1982. In 1997, Dragon introduced “Dragon
NaturallySpeaking,” a speech recognition program for personal computers. It
had a vocabulary of 23,000 words.28
IBM followed with ViaVoice, and other
companies, including Microsoft, also have speech recognition software.
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![17 NOTES
The transcription of spoken sentences to their textual equivalents is now
largely a solved problem. For example, high-quality speech recognition is
commonly employed today in many automated telephone response systems.
However, understanding natural language speech (or text) to permit general
dialogs with computer systems, for example, remains a long-term research
problem. I’ll continue my discussion of work on that problem in a later
chapter.
Notes
1. K. H. Davis, R. Biddulph, and S. Balashek, “Automatic Recognition of Spoken Digits,”
Journal of the Acoustical Society of America, Vol. 24, No. 6, pp. 627–642, 1952. [269]
2. For a history of early work see B. H. Juang and Lawrence R. Rabiner, “Automatic
Speech Recognition – A Brief History of the Technology Development,” available online at
http://www.ece.ucsb.edu/Faculty/Rabiner/ece259/Reprints/
354 LALI-ASRHistory-final-10-8.pdf; or Sadaoki Furui, “50 Years of Progress in Speech and
Speaker Recognition,” available online at
http://www.furui.cs.titech.ac.jp/publication/2005/SPCOM05.pdf. [269]
3. C. Cordell Green, “AI During IPTO’s Middle Years,” in Thomas C. Bartee (ed.), Expert
Systems and Artificial Intelligence: Applications and Management, p. 240, Indianapolis:
Howard W. Sams & Co., 1988. [270]
4. Other members of the group were Jeffrey Barnett of the Systems Development
Corporation, James Forgie of Lincoln Laboratory, C. Cordell Green, then a lieutenant in the
U.S. Army stationed at DARPA, Dennis Klatt of MIT, J. C. R. Licklider, then at MIT,
John Munson of SRI, Raj Reddy of CMU, and William Woods of BBN. [270]
5. The report was published as a special issue of the journal Artificial Intelligence: Allen
Newell et al., Speech Understanding Systems: Final Report of a Study Group, New York:
American Elsevier Publishing Co., Inc., 1973. A draft of the report is available online in the
Newell collection at http:
//diva.library.cmu.edu/webapp/newell/item.jsp?q=box00105/fld08162/bdl0001/doc0001/.
[270]
6. J. R. Pierce, “Whither Speech Recognition?,” Journal of the Acoustical Society of
America, Vol. 46, No. 4, pp. 1049–1051, Part 2, 1969. Also see a rebuttal by Arthur Samuel
and Pierce’s response to Samuel and to other rebuttals in Journal of the Acoustical Society
of America, Vol. 47, No. 6, Part 2, pp. 1616–1617, 1970. [271]
7. For a description of the SRI work, see Donald E. Walker (ed.), Understanding Spoken
Language, New York: Elsevier North-Holland, Inc., 1978. [271]
8. For more details, see William A. Woods, “Motivation and Overview of BBN
SPEECHLIS: An Experimental Prototype for Speech Understanding Research,” IEEE
Transactions on Acoustics, Speech, and Signal Processing, Vol. ASSP-23, No. 1, pp. 2–9,
February 1975. [271]
9. See J. Wolf and William A. Woods, “The HWIM Speech Understanding System,”
Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP ’77,
Vol. 2, pp. 784–787, May 1977; also (for full details) William A. Woods et al., Speech
Understanding Systems – Final Report, BBN Report No. 3438, Vols. I–V, Bolt, Beranek,
and Newman, Inc., Cambridge, MA, 1976. [272]
10. D. Raj Reddy, Lee D. Erman, and Richard B. Neely, “A Model and a System for
Machine Recognition of Speech,” IEEE Transactions on Audio and Electroacoustics, Vol.
282
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![17.4 NOTES
AU-21, No. 3, pp. 229–238, June 1973; and D. Raj Reddy, Lee D. Erman, R. D. Fennell,
and Richard. B. Neely, “The HEARSAY Speech Understanding System: An Example of the
Recognition Processes, in Proceedings of the 3rd International Joint Conference on Artificial
Intelligence, pp. 185–183, Stanford, CA, August 1973. [272]
11. For background on the speech processing work at CMU during this period, see Lee D.
Erman, “Overview of the HEARSAY Speech Understanding Research,” SIGART Newsletter,
No. 56, pp. 9–16, February 1976. [272]
12. James K. Baker, “Stochastic Modeling as a Means of Automatic Speech Recognition,”
doctoral dissertation, Computer Science Department, Carnegie Mellon University,
Pittsburgh, PA, 1975, and James K. Baker, “The DRAGON System – An Overview,” IEEE
Transactions on Acoustics, Speech, and Signal Processing, Vol. ASSP-23, No. 1, February
1975. [272]
13. Allen Newell, “Harpy, Production Systems and Human Cognition, in Ronald A. Cole
(ed.), Perception and Production of Fluent Speech, Hillsdale, NJ: Lawrence Erlbaum
Associates, 1980. Available online as Carnegie Mellon University Technical Report
CMU-CS-78-140 at http:
//diva.library.cmu.edu/webapp/newell/item.jsp?q=box00089/fld06145/bdl0001/doc0001/.
[272]
14. For a translation see A. A. Markov, “An Example of Statistical Investigation of the Text
Eugene Onegin Concerning the Connection of Samples in Chains,” Science in Context, Vol.
19, No. 4, pp. 591-600, 2006. [275]
15. See L. E. Baum and J. A. Eagon, “An Inequality with Applications to Statistical
Estimation for Probabilistic Functions of a Markov Process and to a Model for Ecology,”
Bulletin of the American Medical Society, Vol. 73, pp. 360–363, 1967. Baker credits Baum
with introducing him to the theory of a probabilistic function of a Markov process. [275]
16. James K. Baker, “The DRAGON System – An Overview,” IEEE Transactions on
Acoustics, Speech, and Signal Processing, Vol. ASSP-23, No. 1, p. 24, February 1975. [275]
17. Ibid, p. 29. [275]
18. Bruce T. Lowerre, “The HARPY Speech Recognition System,” doctoral dissertation,
Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, April 1976.
[276]
19. I am basing my description of HARPY on Bruce Lowerre and Raj Reddy, “The HARPY
Speech Understanding System,” Trends in Speech Recognition, Prentice Hall. Reprinted in
A. Waibel and K. Lee (eds.), Readings in Speech Recognition, pp. 576–586, San Mateo, CA:
Morgan Kaufmann Publishers, Inc., 1990. [276]
20. Lee D. Erman, Frederick Hayes-Roth, Victor R. Lesser, and D. Raj Reddy, “The
HEARSAY-II Speech-Understanding System: Integrating Knowledge to Resolve
Uncertainty,” Computing Surveys, Vol. 12, No. 2, June 1980. [278]
21. For a detailed summary of how HEARSAY processed an example sentence, see ibid.
[279]
22. Telephone conversation, August 14, 2008. [280]
23. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, second
edition, p. 580, Upper Saddle River, NJ: Prentice Hall, 2003. [280]
24. Dennis H. Klatt, “Review of the ARPA Speech Understanding Project,” Journal of the
Acoustical Society of America, Vol. 62, No. 2, pp. 1345–1366, December 1977. [280]
25. Lee D. Erman, Frederick Hayes-Roth, Victor R. Lesser, and D. Raj Reddy, op. cit..
[281]
26. Funding a Revolution: Government Support for Computing Research, Chapter 9,
Committee on Innovations in Computing and Communications: Lessons from History,
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![17 NOTES
Computer Science and Telecommunications Board, Commission on Physical Sciences,
Mathematics, and Applications, National Research Council, Washington, DC: National
Academy Press, 1999. Available online at
http://books.nap.edu/openbook.php?record id=6323&page=15. [281]
27. See, for example, Frederick Jelinek, “Continuous Speech Recognition by Statistical
Methods, Proceedings of the IEEE, Vol. 64, No. 4, pp. 532–556, April 1976. [281]
28. Dragon NaturallySpeaking is now available through Nuance. [281]
284
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![18 Consulting Systems
had returned to head IPTO in 1973]. Recollecting impressions of his visit,
Licklider later said7
The second time I was in DARPA, there were very impressive AI
systems dealing with maintenance of equipment. I remember SRI
had a program that described how to take a pump apart and put it
back together. That’s not a terribly complicated device, but it was
pretty impressive to see a computer that obviously understood all
the parts of the pump and how they worked together.
Because of Licklider’s encouragement, we were optimistic about
continuing the CBC project and made plans for a system that would diagnose
and give advice about repairing a military jeep engine. Unfortunately, one of
DARPA IPTO’s new program managers, Colonel David Russell, was not
buying it. After visiting SRI a few days before Licklider’s April 23 visit,
Russell sent an e-mail to Licklider saying8
I must admit to considerable concern over the SRI program,
particularly in light of the management pressures on the AI
program. Looking at the projected program plan that Nils has
been working on, I see a 2.2M dollar program over the next three
years with the aim of developing an experimental CBC for a jeep.
. . . I can’t see how it can be defended as a near-term application. . .
While it may be difficult, I would suggest that you give serious
thought to terminating the CBC program when it completes the
air-compressor phase and redirect SRI to more Defense oriented
applications or pass their work to NSF. I appreciate that this is
heresy, but that is how I saw the situation.
I didn’t directly discuss these comments with Nils although I did
ask what he would do if the program were terminated. I may have
formed a negative view based on an incorrect understanding of the
program, and I didn’t want to upset the SRI group without your
views of the program.
Later that year, Russell replaced Licklider as Director of DARPA IPTO
and terminated the CBC project. (Work on TDUS, however, continued under
NSF support.) DARPA support for the SRI group was subsequently
“redirected” to natural language interfaces to databases (which I’ll describe
later) and to “image understanding” to aid photo interpreters. Some of us
chose instead to seek non-DARPA support to work on computer-based
consulting systems. Ongoing work at Stanford University on so-called expert
systems encouraged us in that direction.
290
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![18 Consulting Systems
“IF–THEN rules” used in DENDRAL would be appropriate. When diagnosing
what disease might be causing certain symptoms, as well as in prescribing
therapy, physicians appear to be using a kind of IF–THEN reasoning: IF the
symptoms are such-and-such, THEN the cause is likely to be so-and-so. The
knowledge behind this sort of reasoning is based on experience with cases as
well as on scientific knowledge about diseases. It was believed that the
IF–THEN knowledge needed by the program could be obtained by
interviewing the appropriate medical experts who already thought in those
terms.
Interestingly, IF–THEN reasoning about medical matters has a long
history. Summarizing part of a book by J. H. Breasted9
about surgical
knowledge contained in an ancient Egyptian papyrus, Robert H. Wilkins wrote
“The Edwin Smith Surgical Papyrus, dating from the seventeenth century
B.C., is one of the oldest of all known medical papyri.”10
(The papyrus was
bought in a Luxor antique shop by Edwin Smith in 1882.) Wilkins goes on to
mention several rules from the papyrus, one of which is the following:
Case Thirty
Title: Instructions concerning a sprain in a vertebra of his neck.
Examination: If thou examinest a man having a sprain in a
vertebra of his neck, thou shouldst say to him: “look at thy two
shoulders and thy breast.” When he does so, the seeing possible to
him is painful.
Diagnosis: Thou shouldst say concerning him: “One having a
sprain in a vertebra of his neck. An ailment which I will treat.”
Treatment: Thou shouldst bind it with fresh meat the first day.
Now afterward thou shouldst treat [with] ywrw (and) honey every
day until he recovers.
Two other experts who joined in the development of the nascent
diagnostic and treatment system were Thomas Merigan, Chief of the
Infectious Disease Division at Stanford, and Stanton Axline, a physician in
that division. In their summary11
of the history of the project, Buchanan and
Shortliffe credit Axline with coming up with the name MYCIN for the program.
The team submitted a successful grant application to the National
Institutes of Health in October of 1973. Shortliffe decided to combine his
medical studies with work toward a Computer Science Ph.D. based on MYCIN.
Since the version of LISP he wanted to use (BBN-LISP, soon to become
INTERLISP) was not available at Stanford, he used the SRI AI group’s PDP-10
computer.
The IF–THEN rules elicited from the medical experts usually were
hedged with uncertainty. Buchanan and Shortliffe mention that “Cohen and
Axline used words such as ‘suggests’ or ‘lends credence to’ in describing the
292
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![18 Consulting Systems
The system then calculated what additional information would be most
effective in altering the probability of whatever it was the geologist wanted to
find out. PROSPECTOR would then ask a question to elicit that information
(and its probability). Throughout the process, the user could volunteer
additional information at any time.
Because PROSPECTOR could use volunteered information, a run of the
program need not be part of a question-and-answer consultation session.
Instead, a user could input a whole set of data about “findings in the field” to
PROSPECTOR, which would then draw its conclusions. These findings could be
from a database or, perhaps more usefully, from a map that indicated contours
of regions in which various kinds of minerals were found to be present. (Kurt
Konolige of SRI joined the PROSPECTOR team around this time and wrote a
program that allowed PROSPECTOR to use map data as an input.)
The most dramatic instance of PROSPECTOR’s use of map data occurred
when it successfully identified the location of a porphyry molybdenum deposit
at Mount Tolman in the state of Washington.24
Results of previous
exploration of the Mount Tolman site were used to produce maps outlining
important geological data relevant to potential molybdenum deposits.
PROSPECTOR processed these maps using rules obtained primarily from Victor
F. Hollister, an expert on porphyry molybdenum deposits, and Alan
Campbell. The result of the processing was another map indicating the
relative “favorability” of a mineral deposit. Computer displays of some of the
input maps are shown in Fig. 18.8. I won’t explain the geological details of
what these maps depict, but they represent the kind of data thought to be
important by experts such as Campbell and Hollister.
From data of this sort, PROSPECTOR produced favorability maps, one of
which is shown in Fig. 18.9. The scale on the right of the map (when rendered
in color) indicates favorability from +5 (highly favorable) through −5 (highly
unfavorable). Based on previous extensive drilling in the largest of the
favorable areas, a mining company had planned an open-pit mine there
(outlined by the contour labeled “proposed pit”).
One must be careful in evaluating this result. It is not the case that
PROSPECTOR discovered an ore deposit in a site previously unexplored. As
was pointed out in a letter to the editor of the journal Artificial Intelligence,25
A large mining company had already found a molybdenum ore
body by drilling over 200 exploration holes in one region. . . and we
knew that they intended to do further drilling for their own
information.
. . .
[This further drilling] showed a remarkable congruence with
PROSPECTOR’s favorability map, including both verification of
PROSPECTOR’s prediction of a large, previously unknown region of
298
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![18.2 Expert Systems
Pittsburgh and the CASNET (Causal-ASsociational NETwork) program by
Casimir A. Kulikowski and Sholom M. Weiss of Rutgers University.
The INTERNIST-1 series of diagnosis programs contained expertise about
internal medicine.29
Part of this knowledge was represented in a kind of
semantic network or taxonomy of disease states (called a nosology in
medicine). In an article in the New England Journal of Medicine, Miller,
Pople, and Myers state that the performance of INTERNIST-1 “on a series of 19
clinicopathological exercises (Case Records of the Massachusetts General
Hospital) published in the Journal appeared qualitatively similar to that of the
hospital clinicians but inferior to that of the case discussants.” However, they
concluded that “the present form of the program is not [yet] sufficiently
reliable for clinical applications.”30
Later, much of the diagnostic knowledge
assembled in INTERNIST-1 was repackaged in QMR (Quick Medical Reference),
a diagnostic decision support system for internists.31
(It has since been
discontinued by its eventual purchaser First DataBank.)
CASNET also used networks.32
In those, “inference rules” linked
observations, patho-physiological states, diagnostic states, and treatment
states. Their primary application was to the glaucomas, for which they had
good physical models on which the inference rules could be based.
At Carnegie Mellon University, John McDermott (1942– ) helped in the
development of a rule-based system called XCON (for eXpert CONfigurer) to
assist in the ordering and configuring of Digital Equipment Corporation’s VAX
computer systems. XCON grew out of an earlier system by McDermott called
R1.33
R1 and XCON were written in a special rule-processing language called
OPS5, one of the OPS family of languages developed by Charles Forgy (1949– )
at CMU.34
(OPS is said to be an acronym for Official Production System.)
The OPS languages used Forgy’s “Rete” algorithm for efficiently stringing
IF–THEN rules together.35
XCON first went into use in 1980 in DEC’s plant in
Salem, New Hampshire.36
The problem with how to deal with uncertain information was avoided in
XCON because it almost never encountered a configuration issue that it did not
have enough certain knowledge to handle. By 1989, according to a paper
about XCON and related configuration systems at DEC,37
these systems had a
total of about 17,500 rules. The paper went on to say that
. . . overall the net return to Digital is estimated to be in excess of
$40 million per year.
The use of the configuration systems insures that complete,
consistently configured systems are shipped to the customer.
Incomplete orders do not get through the process. In addition,
XCON generates configurations which optimize system performance,
so customers consistently get the best view of our products. Before
the configuration systems, we would often ship the same parts
configured differently.
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![18 Consulting Systems
In addition to XCON and its DEC siblings, several expert systems were
built and put in use by companies and research laboratories during the 1980s.
In 1983, General Electric developed the Diesel Electric Locomotive
Troubleshooting Aid (DELTA), a prototype system to assist railroad personnel
in the maintenance of General Electric’s diesel-electric locomotives. The
developers stated that it “can diagnose multiple problems with the locomotive
and can suggest repair procedures to maintenance personnel.” It had 530 rules
“partially representing the knowledge of a Senior Field Service Engineer.”38
Another example is JETA (Jet Engine Troubleshooting Assistant),
developed by engineers at the National Research Council in Canada.
According to a paper about JETA, it “has been applied to troubleshoot the
General Electric J85-CAN-15 jet engine that powers the CF-5 trainer fighters
used by the Canadian Air Force.”39
Knowledge about jet engines and their
possible faults and symptoms are encoded in frames. Rules are used solely for
“specific control functions embedded in a frame and for asynchronous user
input.”
An expert system called CCH-ES for credit analysis was put in use at the
Credit Clearing House (CCH) division of Dun & Bradstreet (D&B) in July
1989. It contained approximately 800 rules and could handle online
transactions when CCH customers called in for service or when analysts
wanted to review cases. Batch cases were run when there were updates in the
relevant databases. According to a paper about the system, “Analyst
agreement with CCH-ES continues to be at approximately 98.5 percent on an
ongoing basis. . . . [It] has been a major success at D&B. It has provided CCH
with an automated credit analyst expert system that can provide expert-level
credit analysis decisions consistently and at a high-quality level. Customers
have uniformly praised the system.”40
More expert systems are described in the book The Rise of the Expert
Company.41
In an appendix to that book, Paul Harmon lists over 130 expert
systems in use during the mid- to late 1980s, including
• Grain Marketing Advisor for helping farmers choose marketing or storage
strategies for their grain crops,
• ACE for helping telephone operating companies reduce the incidence of
phone cable failures,
• IDEA for helping technicians diagnose trouble situations in the Infotron
IS4000 Local Area Network,
• Diag 8100 for helping with the diagnosis of problems and failures in IBM
8100 computers at the Travelers Corporation,
• Intelligent Peripheral Troubleshooter for helping to troubleshoot
Hewlett-Packard disk drives,
302
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![18 NOTES
rules management systems (BRMSs) used later versions of the Rete algorithm.
(In 2005, RulesPower sold some of its assets to Fair Isaac Corporation, an
analytics and decision management technology company, which has since
changed its name to FICO.)
Notes
1. See Nils J. Nilsson et al., “Plan for a Computer-Based Consultant System,” SRI AI
Center, Technical Note 94, May 1974. (Available online at
http://www.ai.sri.com/pubs/files/1298.pdf.)
[285]
2. From Peter E. Hart, “Progress on a Computer Based Consultant,” SRI AI Center
Technical Note 99, p. 23, January 1975. (Available online at
http://www.ai.sri.com/pubs/files/1389.pdf.) [286]
3. Ann E. Robinson, Douglas E. Appelt, Barbara J. Grosz, Gary G. Hendrix, and Jane J.
Robinson, “Interpreting Natural-Language Utterances in Dialogs About Tasks,” SRI AI
Center Technical Note 210, March 15, 1980. (Available online at
http://www.ai.sri.com/pubs/files/709.pdf.) [287]
4. Ibid, p. 11. [287]
5. Ibid, p. 11. [288]
6. Jane J. Robinson, “DIAGRAM,” SRI AI Center Technical Note No. 205, 1980; available
online as SRI AI Center Technical Note 205, February 1980, at
http://www.ai.sri.com/pubs/files/712.pdf. [289]
7. J. C. R. Licklider, “The Early Years: Founding IPTO,” in Thomas C. Bartee (ed.),
Expert Systems and Artificial Intelligence: Applications and Management, p. 223,
Indianapolis, IN: Howard W. Sams & Co., 1988. [290]
8. A copy of this e-mail is in my files. [290]
9. J. H. Breasted, The Edwin Smith Surgical Papyrus, two volumes., Chicago: University
of Chicago Press, 1980. [292]
10. See http://www.neurosurgery.org/cybermuseum/pre20th/epapyrus.html for a copy of
the Wilkins article. [292]
11. Bruce G. Buchanan and Edward H. Shortliffe (eds.), Rule-Based Expert Systems: The
MYCIN Experiments of the Stanford Heuristic Programming Project, Reading, MA:
Addison-Wesley, 1984. The book is now out of print but is available online at
http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/RuleBasedExpertSystems.
Shortliffe’s dissertation has been reprinted as a book: Edward H. Shortliffe, Computer-Based
Medical Consultations: MYCIN, New York: Elsevier, 1976. [292]
12. Others too attempted to use ideas not strictly based on probability theory. Among these
were Arthur Dempster and Glenn Shafer (see Glenn Shafer, A Mathematical Theory of
Evidence, Princeton, NJ: Princeton University Press, 1976) and Lotfi Zadeh, who developed
“fuzzy logic” (see, as just one of many sources, Lotfi A. Zadeh, “A Fuzzy-Algorithmic
Approach to the Definition of Complex or Imprecise Concepts,” International Journal of
Man-Machine Studies, Vol. 8, pp. 249–291, 1976, available online at
http://www-bisc.cs.berkeley.edu/ZadehFA-1976.pdf.) I’ll mention these alternatives later in
the book. [293]
13. For a full description of how MYCIN’s rules were acquired and used see Bruce G.
Buchanan and Edward H. Shortliffe, op. cit.. [293]
304
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![18.2 NOTES
14. Bruce G. Buchanan and Edward H. Shortliffe, op. cit., Chapters 30 and 31. [294]
15. EMYCIN is described in Bruce G. Buchanan and Edward H. Shortliffe, op. cit., Chapter
15. EMYCIN was the subject of van Melle’s Ph.D. dissertation: William van Melle, “A
Domain-Independent System That Aids in Constructing Knowledge-Based Consultation
Programs,” Stanford University Computer Science Department; see also Stanford Report
Nos. STAN-CS-80-820 and HPP-80-22, 1980. [294]
16. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, second
edition, p. 525, Upper Saddle River, NJ: Prentice Hall, 2003. [295]
17. Bruce G. Buchanan and Edward H. Shortliffe, op. cit. [295]
18. Economic geologists distinguish mineral deposits from ore deposits. An ore is a mineral
that can be profitably extracted. Hard-rock minerals include copper, lead, zinc, and so on,
but not hydrocarbons. [295]
19. PROSPECTOR was first described in Peter E. Hart, “Progress on a Computer-Based
Consultant,” Proceedings of the International Joint Conference on Artificial Intelligence,
Vol. 2, pp. 831–841, 1975. [295]
20. Richard O. Duda et al., “Semantic Network Representations in Rule-Based Inference
Systems,” in D. A. Waterman and Frederick Hayes-Roth (eds.), Pattern-Directed Inference
Systems, Orlando, FL: Academic Press, Inc., 1978. Available online at
http://www.ai.sri.com/pubs/files/751.pdf. [296]
21. Gary G. Hendrix, “Partitioned Networks for the Mathematical Modeling of Natural
Language Semantics,” Ph.D. thesis, University of Texas Computer Science Department,
1975. For a short paper, see Gary G. Hendrix, “Expanding the Utility of Semantic Networks
Through Partitioning,” Proceedings of the Fourth International Conference on Artificial
Intelligence, pp. 115–121, 1975. This paper also appeared as SRI AI Center Technical Note
105 and is available online at http://www.ai.sri.com/pubs/files/1380.pdf. [296]
22. For a description of PROSPECTOR’s inference methods see Richard O. Duda, Peter E.
Hart, and Nils J. Nilsson, “Subjective Bayesian Methods for Rule-Based Inference Systems,”
in Proceedings of the AFIPS National Computer Conference, Vol. 45, pp, 1075–1082, 1976.
Reprinted in G. Shafer and J. Pearl (eds.), Readings in Uncertain Reasoning, pp. 274–281,
San Francisco: Morgan Kaufmann Publishers, 1990. A version appears as SRI AI Center
Technical Note 124 and is available online at http://www.ai.sri.com/pubs/files/755.pdf.
[296]
23. Glenn Shafer and Judea Pearl (eds.), Readings in Uncertain Reasoning, San Francisco:
Morgan Kaufmann Publishers, 1990. The book is no longer in print, but some of the
chapters are available online at http://www.glennshafer.com/books/rur.html. [296]
24. Alan N. Campbell, Victor F. Hollister, Richard O. Duda, and Peter E. Hart,
“Recognition of a Hidden Mineral Deposit by an Artificial Intelligence Program,” Science,
Vol. 217, No. 4563, pp. 927–929, September 3, 1982. [298]
25. Richard O. Duda, Peter E. Hart, and René Reboh, letter to the editor, Artificial
Intelligence, Vol. 26, pp. 359–360, 1985. [298]
26. Richard O. Duda, “The PROSPECTOR System for Mineral Exploration,” Final Report
prepared for the Office of Resource Analysis, U.S. Geological Survey, Reston, VA 22090,
April 1980. [299]
27. Richard B. McCammon, “ PROSPECTOR II – An Expert System for Mineral Deposit
Models,” International Journal of Rock Mechanics and Mining Sciences and Geomechanics
Abstracts, Vol. 33, No. 6, pp. 267A–267A(1), September 1996. See also Richard B.
McCammon, “PROSPECTOR II,” in H. J. Antonisse, J. W. Benoit, and B. G. Silverman
(eds.), Proceedings of the Annual AI Systems in Government Conference, pp. 88–92, March
1989, Washington, DC. [299]
28. See Peter Szolovits (ed.), Artificial Intelligence in Medicine, Boulder, CO: Westview
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![18 NOTES
Press, 1982. Available online at http://groups.csail.mit.edu/medg/ftp/psz/AIM82/ch0.html.
[300]
29. Harry E. Pople Jr., “Heuristic Methods for Imposing Structure on Ill-Structured
Problems: The Structuring of Medical Diagnostics,” Chapter 5 in Peter Szolovits (ed.),
Artificial Intelligence in Medicine, Boulder, CO: Westview Press, 1982. Available online at
http://groups.csail.mit.edu/medg/ftp/psz/AIM82/ch5.html. [301]
30. Randolph A. Miller et al., “INTERNIST-1: An Experimental Computer-Based
Diagnostic Consultant for General Internal Medicine,” New England Journal of Medicine,
Vol. 307, pp. 468–76, August 19, 1982. [301]
31. Randolph A. Miller et al., “The INTERNIST-1/Quick Medical Reference Project –
Status Report,” The Western Journal of Medicine, Vol. 145, No. 6, pp. 816–822, 1986.
Available online at
http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=1307155&blobtype=pdf. [301]
32. Casimir A. Kulikowski and Sholom M. Weiss, “Representation of Expert Knowledge for
Consultation: The CASNET and EXPERT Projects,” Chapter 2 in P. Szolovits (ed.),
Artificial Intelligence in Medicine, Boulder, CO: Westview Press, 1982. Available online at
http://groups.csail.mit.edu/medg/ftp/psz/AIM82/ch2.html. [301]
33. John McDermott, “R1: A Rule-Based Configurer of Computer Systems,” Artificial
lntelligence, Vol. 19, No. 1, pp. 39–88, 1980. [301]
34. Charles Forgy, “OPS5 User’s Manual,” Technical Report CMU-CS-81-135, Carnegie
Mellon University, 1981. See also Lee Brownston et al., Programming Expert Systems in
OPS5, Reading, MA: Addison-Wesley, 1985. [301]
35. Charles Forgy, “Rete: A Fast Algorithm for the Many Pattern/Many Object Pattern
Match Problem,” Artificial Intelligence, Vol. 19, pp. 17–37, 1982. [301]
36. See http://en.wikipedia.org/wiki/Xcon. [301]
37. Virginia E. Barker and Dennis E. O’Connor, “Expert Systems for Configuration at
Digital: XCON and Beyond,” Communications of the ACM, Vol. 32, No. 3, pp. 298–318,
March 1989. [301]
38. Piero P. Bonissone and H. E. Johnson Jr., “ DELTA: An Expert System for Diesel
Electric Locomotive Repair,” Proceedings of the Joint Services Workshop on Artificial
Intelligence in Maintenance, Boulder, CO, October 4–6, 1983, AD-A145349, pp. 397–413,
June 1984 (Defense Technical Information Center Accession Number ADA145349.) [302]
39. Phillippe L. Davidson et al., “Intelligent Troubleshooting of Complex Machinery,”
Proceedings of the Third International Conference on Industrial Engineering Applications of
Artificial Intelligence Expert Systems, pp. 16–22, Charleston, South Carolina, USA, July
16–18, 1990. See also M. Halasz et al., “ JETA: A Knowledge-Based Approach to Aircraft
Gas Turbine Engine Maintenance,” Journal of Applied Intelligence, Vol. 2, pp. 25–46, 1992.
[302]
40. Roger Jambor et al., “The Credit Clearing House Expert System,” IAAI-91 Proceedings,
pp. 255–269, 1991. [302]
41. Edward Feigenbaum, Pamela McCorduck, and H. Penny Nii, The Rise of the Expert
Company: How Visionary Companies Are Using Artificial Intelligence to Achieve Higher
Productivity and Profits, New York: Times Books, 1988. [302]
42. http://www.pst.com/. [303]
43. Harmon’s appendix, just cited, lists several companies as does
http://dmoz.org/Computers/Artificial Intelligence/Companies/. [303]
44. From http://www.businessrulesgroup.org/defnbrg.shtml. [303]
45. I thank Paul Harmon, now Executive Editor of Business Process Trends
306
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![18.2 NOTES
(www.bptrends.com), for enlightening me about business rules. [303]
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![19 Understanding Queries and Signals
information-processing organization, and the Defense Department
is certainly such an organization. . . . all of this research should be
kept alive because it had potential military relevance.
By the mid-1970s, however, the pressure to produce militarily useful
systems became much more intense. DARPA, which had been generously
supporting rather undirected basic AI research, started to focus instead on
solving “pressing DoD problems.” Although the director of DARPA’s IPTO at
the time, J. C. R. Licklider, was as sympathetic as ever to basic research in
AI, DARPA’s top management had entirely different attitudes. Licklider was
having difficulties explaining his AI program to DARPA’s “front office.” The
DARPA Director during the early 1970s, Stephen Lukasik, was (according to
Licklider3
)
neither for nor against AI. He was for good management and he
got the idea that maybe some of the AI stuff wasn’t being very well
managed. . . . [He] had a fixed idea that a proposal is not a proposal
unless it’s got milestones. I think that he believed that the more
milestones, the better the proposal. . . . I think he was not
developing a distaste for AI but a conviction that this is such an
important field that the researchers have got to learn to live in a
bigger, more rigid, more structured bureaucracy.”
Lukasik’s view about how projects should be managed had a direct effect
on DARPA-supported basic research in AI. For example, a “Quarterly
Management Report” that I submitted in February 1975 describing progress
on the SRI computer-based consultant caused Licklider to ask how the report
might be recast to emphasize progress along certain paths in a “PERT Chart.”
“What I would like to have,” he wrote me in a letter dated March 3, 1975, “is
the PERT Chart – so that I can mark the accomplishments in red and see
where you stand with respect to the overall pattern. . . . Do you have such a
chart? If so, please send me a copy. If not, how about making one? It would
really help us greatly here at ARPA.”4
Of course, in basic research, although one can describe generally the
problems one is trying to solve, one can’t describe (ahead of time) what the
solutions are going to be. In fact, as exploratory research progresses, new
problems become apparent, so one can’t even describe all the problems ahead
of time. One can’t make the kind of detailed plan for basic research that one
can make for applying already developed technology to specific applications.
Unfortunately, the management of DARPA was shifting from people who
understood how to initiate and manage basic research to people who knew
how to manage technology applications.
The shift toward shorter term, intensely managed research became more
pronounced when George Heilmeier (Fig. 19.1) replaced Stephen Lukasik as
DARPA Director in 1975. Heilmeier came from RCA, where he had headed
310
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![19 Understanding Queries and Signals
development in ARPA that concerns me greatly – and will, I think,
also concern you. . . .
. . . the prevailing direction in ARPA is to do research within the
specific contexts of military problems and not to do research that
does not have a military ‘buyer’ ready to take it over as soon as the
concept gets well formulated. . . .
[there are] strong pressures from the new Director, George
Heilmeier, that IPTO ‘redirect’ the university AI efforts to work on
problems that have real DoD validity. . .
. . . the situation is complicated by the fact that ARPA has been
supporting basic research at a rather high level for more than ten
years (has spent more than $50 million on it), and it is natural for
a new director, or even an old one, to ask, “What have we gotten
out of it in terms of improvements in national defense?”
According to Licklider’s Easter note, some of the things that Heilmeier
thought IPTO could do for the Defense Department were the following:
• get computers to read Morse code in the presence of other code and
noise,
• get computers to identify/detect key words in a stream of speech,
• solve DoD’s “software problem,”
• make a real contribution to command and control, and
• do a good thing in sonar.
Even though one of the items on Heilmeier’s list involved speech
processing, one of the casualties of his tenure as Director of DARPA was the
SUR Program. None of the systems that had been developed under the
program could respond in real time, nor could they deal with large enough
vocabularies. Heilmeier believed (probably with good reason) that speech
understanding was still a basic research activity. Thus, he thought, it should
be supported, say, by the National Science Foundation (NSF), and he rejected
proposals for DARPA to continue it.
Unfortunately, most of the research areas that were on Licklider’s own list
(which was also mentioned in his Easter note) were not explicitly on
Heilmeier’s. (I can’t resist mentioning one of the items on Licklider’s list:
“Develop a system that will guide not-sufficiently-trained maintenance men
through the maintenance of complex equipment.”) One of Heilmeier’s items
was sufficiently vague, however, to justify work both in NLP and in computer
vision. That was “command and control,” an activity that involves getting
and presenting relevant information to commanders so that they can control
military forces effectively.
312
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![19.2 Natural Language Access to Computer Systems
DARPA program officers Floyd Hollister and Col. David Russell were
able to persuade DARPA management that text-based, natural language
access to large, distributed databases would be an important component of
command and control systems. They argued that the technology for such
access was sufficiently far along for it to be applied in what they called
“command-and-control test-bed systems.” After all, Bill Woods and colleagues
at BBN had already demonstrated LUNAR, a natural language “front end” to
databases about moon rocks. Several other researchers had also begun work
on the problem of how to communicate with computers using English or some
other natural language. (For example, there were over forty papers on NLP
presented at the Fifth IJCAI in 1977 at MIT, and the February 1977 issue of
the ACM’s SIGART Newsletter published 52 summaries of ongoing research
on “Natural Language Interfaces.”) In the next part of this chapter, I’ll
describe some of the accomplishments during this period on communicating
with computers using natural language.
A second area of great importance in command and control was
automating the analysis of aerial photos. Spotting targets of military interest
in these photos, such as roads, bridges, and military equipment, typically
required hours of effort by intelligence analysts. Because techniques being
developed by researchers in computer vision might provide tools to help
human analysts, DARPA had good reasons to continue funding computer
vision research. In 1976, it began the “Image Understanding ”(IU) program to
develop the technology required for automatic and semiautomatic
interpretation and analysis of military photographs and related images.
Although initially conceived as a five-year program, it continued (with broader
objectives) for well over twenty years. I’ll summarize the image understanding
work, along with other computer vision research, in a subsequent chapter.
Doing something about sonar was one of the items on Heilmeier’s list. In
fact, in his Easter note Licklider wrote “One of [Heilmeier’s] main silver-bullet
areas is underwater sound and sonar, and IPTO is in the process of ‘buying in’
on the HASP project (Ed Feigenbaum’s AI approach).” I’ll describe HASP and
how DARPA “bought in” to the project toward the end of the chapter.
19.2 Natural Language Access to Computer
Systems
19.2.1 LIFER
At SRI, Gary Hendrix (Fig. 19.2) had been developing a system called LIFER
(an acronym for Language Interface Facility with Elliptical and Recursive
Features), programmed in INTERLISP, for rapid development of natural
language “front ends” to databases and other software. LIFER allowed a
nontechnical user to specify a subset of a natural language (for example,
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![19.2 Natural Language Access to Computer Systems
grammars based on concepts in the domain of application are called “semantic
grammars.”
LIFER used a simplified augmented transition network (like those I
described in a previous chapter) to analyze an input sentence. Each pattern
defined by the grammar corresponded to a possible “path” in the transition
network. An input sentence was analyzed by attempting to match it with one
of these paths, noting which specific instance of a wild card, such as
<ATTRIBUTE>, was used in the match. Depending on the path taken and on
the values of wild cards in the path, software was automatically created that
was then used to make the appropriate database query or to carry out an
appropriate command.6
In 1982, Hendrix left SRI to form Symantec, a
company that planned to develop and market a natural language
question-answering system based on semantic grammars such as LIFER.
[Perhaps natural language processing (or the intended market) was not quite
ready, because Symantec was later reorganized to market computer security
and anti-virus software.]
LIFER was used at SRI as the natural language component of a system
called “LADDER” for accessing multiple, distributed databases.7
LADDER (an
acronym for Language Access to Distributed Data with Error Recovery)
translated the English query into a hypothetical database query that assumed
a very simple database organization. Using a system called IDA (an acronym
for Intelligent Data Access), that hypothetical query was transformed into a
series of actual database queries that took into account the actual organization
of the database. It also took account of syntactic and semantic knowledge to
attempt to produce very efficient queries and to detect any erroneous updates
to the database content. (More research on systems similar to IDA was
performed in a joint program between Stanford University and SRI, named
KBMS, an acronym for Knowlege Based Management System, with support
from DARPA.)
Consistent with DARPA’s focus on military applications, LADDER was
able to answer questions about naval ships using information about ship sizes,
types, locations, and so on from various databases. Some sample interactions
with an early version of LADDER are shown in Fig. 19.3. Note the ability of
the system to correct spelling errors, to deal with incomplete questions, and to
accept paraphrases.8
19.2.2 CHAT-80
Between 1979 and 1982, Fernando Pereira (1952– ) and David H. D. Warren
(circa 1950– ) developed a system called CHAT-80 at the University of
Edinburgh as part of Pereira’s Ph.D. dissertation there. CHAT-80 was able to
answer rather complex questions, posed in English, about a database of
geographical facts.
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![19.3 NOTES
Longitude 123.1 at time day 2, 4 hours, 55 minutes, can be either
Cherry, Iris, Tulip, or Poppy class. Two distinct acoustic sources,
supported by respective harmonic sets, have been identified for
Vessel-l. Source-l could be due to a shaft or propeller of vessel class
Cherry or Poppy. Similar source possibilities exist for Source-5.
These two sources were assimilated into Vessel-l because of the
possibility of a known mechanical ratio that exists between the two
sources.
The MITRE Corporation conducted several experiments to compare the
performance of HASP/SIAP against that of two expert sonar analysts. In one
of these experiments, MITRE concluded that “HASP/SIAP has been shown to
perform well on ocean derived data. . . For this restricted ocean scene, the
program is not confused by extraneous data and gives results comparable to
an expert analyst.” In another experiment, it concluded that “HASP/SIAP
understood the ocean scene more thoroughly than the second analyst and as
well as the first analyst. . . The program can work effectively with more than
one acoustic array. SIAP classified an ocean scene over a three hour time
period indicating the plausibility of SIAP efficacy in an evolving ocean
situation.” The third experiment led to the conclusions that “with the
exception that the SIAP program obtained significantly more contacts than the
human analysts, the descriptions of the ocean scene are very similar.”
Moreover, “SIAP can perform vessel classification in increasingly difficult ocean
scenes without large increases in the use of computer resources.”22
As mentioned earlier, the Blackboard model has been applied in several
other areas as well. Examples include protein crystallographic analysis,23
image understanding,24
and dialog comprehension.25
Interestingly, the
Blackboard architecture has impacts beyond technology. Donald Norman, a
cognitive psychologist, has said that HEARSAY-II has been a source of ideas for
theoretical psychology and that it fulfills his “intuitions about the form of a
general cognitive processing structure.”26
Also, as I’ll mention in a later
chapter, several models of the neocortex involve interacting layers resembling
both the form and the mechanisms of Blackboard systems.
Notes
1. Lawrence G. Roberts, “Expanding AI Research and Founding Arpanet,” in Thomas C.
Bartee (ed.), Expert Systems and Artificial Intelligence: Applications and Management, pp.
229–230, Indianapolis, IN: Howard W. Sams & Co., 1988. [309]
2. C. Cordell Green, “AI During IPTO’s Middle Years,” ibid, pp. 238–240. [309]
3. J. C. R. Licklider, “The Early Years: Founding IPTO,” ibid, pp. 225–226. [310]
4. Licklider letter in my file. [310]
5. J. C. R. Licklider, op. cit., p. 226. [311]
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![19 NOTES
6. For technical details about LIFER, see Gary G. Hendrix, “LIFER: A Natural Language
Interface Facility,” SRI AI Center Technical Note 135, December 1976 (available online at
http://www.ai.sri.com/pubs/files/1414.pdf); Gary G. Hendrix, “The LIFER Manual: A
Guide to Building Practical Natural Language Interfaces,” SRI AI Center Technical Note
138, February 1977 (available online at http://www.ai.sri.com/pubs/files/749.pdf); and
Gary G. Hendrix, “Human Engineering for Applied Natural Language Processing,”
Proceedings of the 5th IJCAI, pp. 183–191, 1977 (which also appeared as SRI AI Center
Technical Note 139, available online at http://www.ai.sri.com/pubs/files/748.pdf). [315]
7. Earl D. Sacerdoti, “Language Access to Distributed Data with Error Recovery,”
Proceedings of the 5th IJCAI, pp. 196–202, 1977, and reprinted as SRI AI Center Technical
Note 140, February 1977 (available online at http://www.ai.sri.com/pubs/files/747.pdf);
Earl D. Sacerdoti, “A LADDER User’s Guide (Revised),” SRI AI Center Technical Note
163R, March 1980 (available online at http://www.ai.sri.com/pubs/files/735.pdf); and Gary
G. Hendrix et al. “Developing a Natural Language Interface to Complex Data,” ACM
Transactions on Database Systems, Vol. 3, No. 2, pp. 105-147, June 1978 (available online
as SRI AI Center Technical Note 152, August 1977, at
http://www.ai.sri.com/pubs/files/741.pdf). [315]
8. For a more extensive interaction with a later version of LADDER, see Appendix A of
Earl D. Sacerdoti, “A LADDER User’s Guide (Revised),” SRI AI Center Technical Note
163R, March 1980. [315]
9. Fernando Pereira, “Logic for Natural Language Analysis”, Ph.D. dissertation, University
of Edinburgh, 1982. A slightly revised version of the dissertation was published as Technical
Note 275 of the SRI AI Center and is available online at
http://www.ai.sri.com/pubs/files/669.pdf. [316]
10. Readers interested in the details of these rather technical processes might refer to
Pereira’s dissertation or to David H. D. Warren and Fernando Pereira, “An Efficient Easily
Adaptable System for Interpreting Natural Language Queries,” Computational Linguistics,
Vol. 8 , Nos. 3–4, pp. 110–122, July–December 1982. [317]
11. Bozena H. Thompson and Frederick B. Thompson, “Introducing ASK, A Simple
Knowledgeable System,” Conference on Applied Natural Language Processing, pp. 17–24,
1983. Available online at http://ucrel.lancs.ac.uk/acl/A/A83/A83-1003.pdf. [318]
12. Marjorie Templeton and John Burger, “Problems in Natural Language Interface to
DBMS with Examples from EUFID,” Proceedings of the First Conference on Applied
Natural Language Processing, pp. 3–16, 1983. Available online at
http://www.aclweb.org/anthology-new/A/A83/A83-1002.pdf. [318]
13. Madeleine Bates and Robert J. Bobrow, “A Transportable Natural Language Interface,”
Proceedings of the 6th Annual International ACM SIGIR Conference on Research and
Development in Information Retrieval, pp. 81–86, 1983. [318]
14. Bruce Ballard, John C. Lusth, and Nancy L. Tinkham, “LDC-1: A Transportable,
Knowledge-Based Natural Language Processor for Office Environments,” ACM Transactions
on Information Systems, Vol. 2, No. 1, pp. 1–25, January 1984. [318]
15. Jerrold M. Ginsparg, “A Robust Portable Natural Language Data Base Interface,”
Conference on Applied Natural Language Processing, pp. 25–30, 1983. Available online at
http://ucrel.lancs.ac.uk/acl/A/A83/A83-1004.pdf. [318]
16. Barbara J. Grosz et al., “TEAM: An Experiment in the Design of Transportable
Natural-Language Interfaces,” Artificial Intelligence, Vol. 32, No. 2, pp. 173–243, May
1987. Available online as SRI Technical Note 356R, October 20, 1986, at
http://www.ai.sri.com/pubs/files/601.pdf. [318]
17. Barbara Grosz et al., “DIALOGIC: A Core Natural-Language Processing System,”
Proceedings of Ninth International Conference on Computational Linguistics, pp. 95–100,
1982. Available online at http://www.aclweb.org/anthology-new/C/C82/C82-1015.pdf.
[318]
324
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![19.3 NOTES
18. DIAMOND was developed at SRI by William Paxton and is described in Ann E.
Robinson et al., “Interpreting Natural-Language Utterances in Dialogs About Tasks,” AI
Center Technical Note 210, SRI International, March 1980. Available online at
http://www.ai.sri.com/pubs/files/709.pdf. [318]
19. Jane J. Robinson, “DIAGRAM: A Grammar for Dialogs,” Communications of the ACM,
Vol. 25, No. 1, pp. 27–47, January 1982. Available online as SRI AI Center Technical Note
205, February 1980, at http://www.ai.sri.com/pubs/files/712.pdf. [318]
20. H. Penny Nii, Edward A. Feigenbaum, John J. Anton, and A. J. Rockmore,
“Signal-to-Symbol Transformation: HASP/SIAP Case Study,” AI Magazine, Vol. 3, No. 2,
pp. 23–35, 1982. [320]
21. From ibid, p. 28. [322]
22. From ibid, p. 34. [323]
23. Robert S. Engelmore and H. Penny Nii, “A Knowledge-Based System for the
Interpretation of Protein X-Ray Crystallographic Data,” Stanford Computer Science
Department Technical Report CS-TR-77-589, 1977; available online at
ftp://reports.stanford.edu/pub/cstr/reports/cs/tr/77/589/CS-TR-77-589.pdf. [323]
24. A. R. Hanson and E. M. Riseman, “VISIONS: A Computer System for Interpreting
Scenes,” in A. Hanson and E. Riseman (eds.), Computer Vision Systems, pp. 303– 333, New
York: Academic Press, 1978. [323]
25. W. C. Mann, “Design for Dialogue Comprehension,” in Proceedings of the 17th Annual
Meeting of the Association of Computational Linguistics, pp. 83–84, La Jolla, CA, August
1979; available online at http://ucrel.lancs.ac.uk/acl/P/P79/P79-1020.pdf. [323]
26. Donald A. Norman, “Copycat Science or Does the Mind Really Work by Table
Look-up?,” in R. Cole (ed.), Perception and Production of Fluent Speech, Chapter 12,
Hillsdale, NJ: Lawrence Erlbaum Associates, Inc., 1980. [323]
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![20.2 Finding Objects in Scenes
perception. To do so we will need to draw upon what we have
learned in many fields, including neuroscience, neural networks,
experimental psychology and computational vision.
20.2 Finding Objects in Scenes
20.2.1 Reasoning about Scenes
Even before the development of shape-from-shading and other methods for
recovering depth information from scenes, a number of researchers had worked
on methods for finding objects in scenes. I described many of these techniques
in Section 9.3.
During the early 1970s, Thomas Garvey completed a Stanford Ph.D.
thesis on a system for locating objects, such as desks, chairs, and
wastebaskets, in images of office scenes.9
As Garvey wrote in his summary,
The system uses information about the appearances of objects,
about their interrelationships, and about available sensors to
produce a plan for locating specified objects in images of room
scenes.
In related work, Barrow and Tenenbaum developed a system, called
MSYS, for reasoning about scenes “in which knowledge sources compete and
cooperate until a consistent explanation of the scene emerges by consensus.”10
MSYS analyzed images of office scenes and attempted to find the most likely
interpretation for the regions in an image (desk top, back of chair, floor,
doorway, and so on) given a number of candidate interpretations and their
probabilities. Knowing relationships between regions (such as “chair backs are
usually adjacent to chair seats”), MSYS tried to find the most likely overall set
of region interpretations.
An example of a scene considered by MSYS is shown in Fig. 20.6. Some of
the regions in the scene have been detected and labeled with possible
interpretations.
As Barrow and Tenenbaum wrote, MSYS’s reasoning might proceed as
follows:
Regions PIC, WBSKT, and CBACK cannot be WALL or DOOR,
because their brightnesses are much less than that along the top
edge of the image vertically above them, which violates [knowledge
about the brightness of walls and doors]. Consequently, region PIC
must be the PICTURE, WBSKT must be WASTEBASKET, and
CBACK must be CHAIRBACK.
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![20.2 Finding Objects in Scenes
20.2.2 Using Templates and Models
Much of the early work on object recognition was based on using object
“templates” that could be matched against images. Martin A. Fischler and
Robert A. Elschlager elaborated this idea by using “stretchable templates”
that permitted more powerful matching techniques. They used these to find
objects such as faces or particular terrain features in photographs containing
such objects.11
The process depended on having a general representation for
the object being sought and then a process for matching that representation
against the photograph. Their representations were based on breaking an
object down into a number of primitive parts and “specifying an allowable
range of spatial relations which these ‘primitive parts’ must satisfy for the
object to be present.” For the object to be present in a picture, “it is required
that [the] primitives occur (or at least that some significant subset of them
occurs), and also that they occur within a certain spatial relationship one to
the other. . . ” As Fischler and Elschlager pointed out, it is usually the case
that determining whether or not some of the parts occur depends on whether
or not the whole object occurs, and vice versa. The main contribution of their
paper was the development of a dynamic-programming-style method for
dealing with this circularity.
Earlier I had described David Marr’s work on processes for producing a
primal sketch and a 21
2 -D sketch. These were the first two stages in Marr’s
theory of vision. He argued that these stages could uncover important shape
information without specific knowledge of the shapes of objects likely to be in
a scene. He had written:12
Most early visual processes extract information about the visible
surfaces directly, without particular regard to whether they happen
to be part of a horse, or a man, or a tree. . . . As for the question of
what additional knowledge should be brought to bear, general
knowledge must be enough – general knowledge embedded in the
early visual processes as general constraints, together with the
geometrical consequences of the fact that the surfaces co-exist in
three-dimensional space.
Specific knowledge about shapes, he argued, should be utilized in a third
stage. It is this stage that uses three-dimensional models of objects. He
proposed using a hierarchy of models in which a gross model is decomposed
into subparts and these into subsubparts and so on. For example, the shape of
a human might be modeled as in Fig. 20.7. Each box corresponds to a 3-D
model and its submodel. On the left side of the box is an axis-oriented model;
on the right side is how that model is represented as submodels. (Directions of
the axes can be adjusted to fit matching parts of the image.)
In this third stage, comparing models of this sort with shape information
and other 3-D information contained in the 21
2 -D sketch helps to identify and
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![20 Progress in Computer Vision
Figure 20.7: An example of one of Marr’s 3-D model hierarchies. (From David
Marr, Vision, San Francisco: W. H. Freeman and Co., p. 306, 1982.)
locate objects in a scene. For Marr, vision was “the process of discovering from
images what is present in the world and where it is.”13
Marr was not the first to suggest the use of cylinders as models of parts of
objects. In a 1971 IEEE conference paper, Thomas O. Binford (1936– )
introduced the idea of “generalized cylinders” (sometimes called “generalized
cones”).14
A later paper defined them as follows: “A generalized cone is
defined by a planar cross section, a space curve spine, and a sweeping rule. It
represents the volume swept out by the cross section [not necessarily a circular
one] as it is translated along [an axis called a spine], held at some constant
angle to the spine, and transformed according to the sweeping rule.”15
Binford had several Stanford Ph.D. students who used models to help
identify objects in scenes. Of these I might mention Gerald J. Agin,16
Ramakant Nevatia,17
and Rodney A. Brooks (1954– ),18
all of whom
contributed to what came to be called “model-based vision.” (Brooks later
became a professor at MIT, where he worked on other topics. His subsequent
work will be discussed later.)
Brooks’s ACRONYM system19
used generalized cones to model several
different kinds of objects. ACRONYM used these models to help identify and
locate objects in images. Some examples of the kinds of generalized cones that
can be used as building blocks of models and model objects are shown in Fig.
20.8.
Other views regarding what vision is all about competed with those of
Marr and others who were attempting to use vision to reconstruct entire
scenes. Some, especially those involved in robotics, claimed that the purpose
of vision was to perceive just what was required to guide action. Many of the
336
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![20.2 Finding Objects in Scenes
Figure 20.8: Primitive generalized cones and piston models constructed from
generalized cones. (From Rodney A. Brooks, “Symbolic Reasoning among 3-D
Models and 2-D Images,” Artificial Intelligence, Vol. 17, Nos. 1–3, pp. 285–348,
1981.)
vision routines in Shakey were embedded in its action programs. Professor
Yiannis Aloimonos at the University of Maryland is one of the researchers
advocating this “purposive” or “interactive” approach. He claims that the goal
of vision is action. When vision is “considered in conjunction with action, it
becomes easier.” He goes on to explain that “the descriptions of space-time
that the system needs to derive are not general purpose, but are purposive.
This means that these descriptions are good for restricted sets of tasks, such
as tasks related to navigation, manipulation and recognition.”20
In the
neuroscience community, to which Marr wanted to make a contribution, there
were Patricia S. Churchland, V. S. Ramachandran, and Terrence J. Sejnowski,
who later wrote “What is vision for? Is a perfect internal recreation of the
three-dimensional world really necessary? Biological and computational
answers to these questions lead to a conception of vision quite different from
pure vision [as advocated by Marr]. Interactive vision. . . includes vision with
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![20.3 NOTES
Notes
1. For an extensive list of computer vision applications see the CVonline Web site at
http://homepages.inf.ed.ac.uk/rbf/CVonline/applic.htm. [327]
2. Berthold K. P. Horn, “Shape from Shading: A Method for Obtaining the Shape of a
Smooth Opaque Object from One View,” MIT Department of Electrical Engineering Ph.D.
thesis, MIT Artificial Intelligence Laboratory Technical Report 232, November 1970;
available online at http://people.csail.mit.edu/bkph/AIM/AITR-232-OCR-OPT.pdf. In his
thesis, Horn credits Thomas Rindfleisch’s 1966 work on using image brightness in studies of
lunar topography. [327]
3. For a modern discussion of the problem, see, for example, Emmanuel Prados and Olivier
Faugeras, “Shape from Shading,” in N. Paragios, Y. Chen, and O. Faugeras (eds.), Handbook
of Mathematical Models in Computer Vision, pp. 375–388, New York: Springer-Verlag,
2006; available online at
http://perception.inrialpes.fr/Publications/2006/PF06a/chapter-prados-faugeras.pdf. [328]
4. David Marr, Vision: A Computational Investigation into the Human Representation
and Processing of Visual Information, San Francisco: W. H. Freeman and Co., 1982. [329]
5. Harry G. Barrow and Jay Martin Tenenbaum, “Recovering Intrinsic Scene
Characteristics from Images,” in A. Hanson and E. Riseman (eds.), Computer Vision
Systems, pp. 3–26, New York: Academic Press, 1978. Available online at
http://web.mit.edu/cocosci/Papers/Barrow-Tenenbaum78.pdf and at
http://www.ai.sri.com/pubs/files/737.pdf. [329]
6. Harry G. Barrow and Jay Martin Tenenbaum, “Retrospective on ‘Interpreting Line
Drawings as Three-Dimensional Surfaces,’” Artificial Intelligence, Vol. 59, Nos. 1–2, pp.
71–80, 1993. [331]
7. Harry G. Barrow and Jay Martin Tenenbaum, “Interpreting Line Drawings as
Three-Dimensional Surfaces,” Artificial Intelligence, Vol. 17, pp. 75–116, 1981. Available
online at http://web.mit.edu/cocosci/Papers/Barrow-Tenenbaum81.pdf. [331]
8. Harry G. Barrow and Jay Martin Tenenbaum, “Retrospective on ‘Interpreting Line
Drawings as Three-Dimensional Surfaces,’” Artificial Intelligence, Vol. 59, Nos. 1–2, pp.
71–80, 1993. [332]
9. Thomas D. Garvey, “Perceptual Strategies for Purposive Vision,” Stanford University
Ph.D. thesis, published as SRI International AI Center Technical Note 117, September 1976.
Abstract available online at http://www.ai.sri.com/pub list/759. [333]
10. Harry G. Barrow and J. Martin Tenenbaum, “MSYS: A System for Reasoning about
Scenes,” SRI International AI Center Technical Note 121, April 1976. Available online at
http://www.ai.sri.com/pubs/files/757.pdf. [333]
11. Martin A. Fischler and Robert A. Elschlager, “The Representation and Matching of
Pictorial Structures,” IEEE Transactions on Computers, Vol. C-22, No. 1, pp. 67–92,
January 1973. [335]
12. David Marr, op. cit., pp. 272–4. [335]
13. David Marr, op. cit., pp. 23–60. [336]
14. Thomas O. Binford, “Visual Perception by Computer,” Proceedings of the IEEE
Conference on Systems and Control, Miami FL, December 1971. [336]
15. Rodney A. Brooks, “Symbolic Reasoning among 3-D Models and 2-D Images,” Artificial
Intelligence, Vol. 17, Nos. 1–3, pp. 285–348, 1981. [336]
16. Gerald J. Agin, “Representation and Description of Curved Objects,” Stanford
University Ph.D. thesis, published as Stanford Artificial Intelligence Project Memo AIM-173,
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![20 NOTES
October 1972. See also Gerald J. Agin and Thomas O. Binford, “Computer Descriptions of
Curved Objects,” Proceedings of the Third International Joint Conference on Artificial
Intelligence, pp. 629–640, August 1973; later published as Gerald J. Agin and Thomas O.
Binford, “Computer Descriptions of Curved Objects,” lEEE Transactions on Computers,
Vol. 25, No. 4, April 1976. [336]
17. Ramakant Nevatia, “Structured Descriptions of Complex Curved Objects for
Recognition and Visual Memory,” Stanford University Department of Electrical Engineering
Ph.D. thesis, published as Stanford Artificial Intelligence Laboratory Memo AIM-250,
October 1974. [336]
18. Rodney A. Brooks, “Symbolic Reasoning among 3-D Models and 2-D Images,” Stanford
University Computer Science Department Ph.D. thesis, 1981, published as Stanford CS
Department Report STAN-CS-81-861. Also published as Rodney A. Brooks, op. cit.. [336]
19. The system was first reported in Rodney A. Brooks, Russell Greiner, and Thomas O.
Binford, “The ACRONYM Model-Based Vision System,” Proceedings of the Sixth
International Joint Conference on Artificial Intelligence, pp. 105–113, Tokyo, 1979. A later
revised version was reported in Brooks’s Artificial Intelligence paper just cited. [336]
20. From his Web page at http://www.cfar.umd.edu/∼yiannis/. [337]
21. Patricia S. Churchland, V. S. Ramachandran, and Terrence J. Sejnowski, “A Critique of
Pure Vision,” in Christof Koch and Joel L. Davis (eds.), Large-Scale Neuronal Theories of
the Brain, pp. 23–65, Cambridge, MA: MIT Press, 1994. Available online at http:
//philosophy.ucsd.edu/faculty/pschurchland/papers/kochdavis94critiqueofpurevision.pdf.
[338]
22. Martin A. Fischler, private communication, August 1, 2007. [338]
23. Jay Martin Tenenbaum, private communication, July 31, 2007. [338]
24. Private communication, July 31, 2007. [338]
25. Lee S. Bauman (ed.), Proceedings: Image Understanding Workshop, Science
Applications, Inc., Report No. SAI-78-549-WA, April 1977. [338]
26. Quoted in the Foreword of the Proceedings: Image Understanding Workshop, published
by Science Applications, Inc., May 1978. [339]
27. Quoted in the Foreword of the Proceedings: Image Understanding Workshop, published
by Science Applications, Inc., November 1978. [339]
28. Quoted in the Foreword of the Proceedings: Image Understanding Workshop, published
by Science Applications, Inc., November 1979. [339]
29. Quoted in the Foreword of the Proceedings: Image Understanding Workshop, published
by Science Applications International Corporation, December 1985. [340]
30. Alex Roland with Philip Shiman, Strategic Computing: DARPA and the Quest for
Machine Intelligence, p. 220, Cambridge, MA: MIT Press, 2002. [340]
31. Richard O. Duda and Peter E. Hart, Pattern Classification and Scene Analysis, New
York: John Wiley and Sons, Inc., 1973. [340]
32. Michael Brady, Computer Vision, Amsterdam: North-Holland Publishing Co., 1981, and
Dana H. Ballard and C. M. Brown, Computer Vision, New York: Prentice Hall, Inc., 1982.
[340]
342
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![21 Boomtimes
systems. In 1984 Fritz Kunze, a graduate student at UC Berkeley, founded
Franz, Inc., to market FranzLISP, a version of the LISP programming language.2
Lavish exhibits at trade shows associated with AI conferences charged the
whole field with excitement. Membership in the AAAI rose from around 5,000
shortly after the society’s founding to a peak of 16,421 in 1987. (AAAI
membership has since leveled off, after the boom, back to around 5,000.) Most
of these new members – curious about what AI could do for them – came from
industry and government agencies. Tutorials about various AI topics at both
AAAI and IJCAI conferences were very well attended by people from industry
wanting to learn about this newly important field.
During the early 1980s, my colleagues in several departments at SRI,
especially those working on Defense Department projects, were eager to get
help from the SRI AI Center – of which I was the director at the time. Mainly,
I thought, they wanted us to “sprinkle a little AI” on their proposed projects
to make them more enticing to government sponsors.
Figure 21.1: Scene from one of the AAAI trade shows during the 1980s. (Pho-
tograph from Bruce B. Buchanan, “Some Recollections about the Early Days
of AAAI,” AI Magazine, Vol. 26, No. 4, p. 14, c 2005 Association for the
Advancement of Artificial Intelligence. Used with permission.)
Reporting on this increasing interest in 1984, the science writer George
Johnson wrote3
“We’ve built a better brain,” exclaimed a brochure for [an expert
system called] TIMM, The Intelligent Machine Model: “Expert
344
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![21.0 NOTES
systems reduce waiting time, staffing requirements and bottlenecks
caused by the limited availability of experts. Also, expert systems
don’t get sick, resign, or take early retirement.” Other companies,
such as IBM, Xerox, Texas Instruments, and Digital Equipment
Corporation, were more conservative in their pronouncements. But
the amplified voices of their salesmen, demonstrating various wares
[in the 1984 AAAI exhibit hall], sounded at times like carnival
barkers, or prophets proclaiming a new age.
The boom continued with Japan’s “Fifth Generation Computer Systems”
project. That project in turn helped DARPA justify its “Strategic Computing
Initiative.” It also helped to provoke the formation of similar research efforts in
Europe (such as the ALVEY Project in the United Kingdom and the European
ESPRIT programme) as well as the formation of American industrial consortia
for furthering advances in computer hardware. Assessments of some of AI’s
difficulties and achievements, compared to some of its promises, led to the end
of the boom in the late 1980s – causing what some called an “AI winter.” I’ll
be describing all of these topics in subsequent chapters.
Notes
1. By the way, the “iwhois” Web site (http://www.iwhois.com/oldest/) lists Symbolics as
having the oldest registered “.com” domain name (registered on March 15, 1985.) [343]
2. See http://www.franz.com/about/company.history.lhtml. [344]
3. George Johnson, “Thinking about Thinking,” APF Reporter, Vol. 8, No. 1, 1984.
Available online at http://www.aliciapatterson.org/APF0801/Johnson/Johnson.html. [344]
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![22.2 Some Impacts of the Japanese Project
22.2.2 The Alvey Program
In March 1982, the British government set up a committee “to advise on the
scope for a collaborative research programme in information technology (IT)
and to make recommendations.” It was chaired by Mr. John Alvey, a senior
director of British Telecommunications. In its report, issued later that year
and titled “A Programme for Advanced Information Technology,” the
committee noted that the Japanese FGCS project was seen “as a major
competitive threat” and that anticipated responses to it by the United States
“would create an equal if not greater degree of competition for the UK
industry.”13
The report recommended “a five-year programme to mobilise the
UK’s technical strengths in IT, through a Government-backed collaborative
effort between industry, the academic sector and other research organisations.
The goal [was to develop] a strong UK capability in the core enabling
technologies, essential to Britain’s future competitiveness in the world IT
market.” The four major technical areas identified for support were “Software
Engineering, Man Machine Interfaces (MMI), Intelligent Knowledge Based
Systems (IKBS) and Very Large Scale Integration (VLSI).” The recommended
budget was £350 million, with the government contributing two-thirds of the
cost and industry the rest.
In 1983, the UK Government accepted the committee’s report and
initiated the “Alvey Programme” to carry out the committee’s
recommendations. A new Directorate, headed by Brian Oakley, Secretary of
the Science and Engineering Research Council (SERC), was set up in the
Department of Trade and Industry (DTI) to coordinate the program.
Sponsorship and funds were provided by DTI, the Ministry of Defence (MoD),
SERC, and industry. Among its other accomplishments, the Alvey program
helped revitalize AI research in Britain. According to Oakley, “If the Lighthill
Report of the early 1970s was paradise lost for the AI community, the Alvey
Report of the early 1980s was paradise regained.”14
The program reached a peak level of funding of around £45 million in
1987 and went on until 1991. It is credited with energizing Britain’s computer
science community by expanding research and development efforts in both
academia and industry. In their excellent summary of the program, published
in 1990, Brian Oakley and Kenneth Owen describe Alvey’s contributions in
AI, parallel architecture, VLSI, integrated circuit CAD, software engineering,
and speech technology.15
22.2.3 ESPRIT
In 1983, the European Economic Community (the predecessor of the European
Union) launched its ESPRIT program. (ESPRIT is an acronym for European
Strategic Program of Research in Information Technology.) According to Luc
Steels and Brice Lepape, who wrote an article focusing on the AI aspects of
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![22.2 NOTES
be obtained from its Web page at
http://www.dfki.de/web/welcome?set language=en&cl=en.
In the United States, a DARPA program analogous to Alvey and ESPRIT
got underway in the early 1980s. It was partially a response to the Japanese
FGCS project, but it also owed much to the observation that the time was
ripe for a major program that would take advantage of ongoing technical
developments in communications technology and in computer hardware and
software. I’ll describe the DARPA program in the next chapter.
Notes
1. “Research Report on Fifth Generation Computer Systems Project,” ICOT Progress
Report, March 1983. [349]
2. Ibid. [350]
3. Shunichi Uchida, “FGCS Project: Knowledge Information Processing by Highly Parallel
Processing,” Institute for New Generation Computer Technology (ICOT), Tokyo, Japan,
undated. Available online at
http://www.icot.or.jp/ARCHIVE/PICS/OHP/Uchi1-FGohpE.pdf. [350]
4. ICOT Staff (eds.) Proceedings of the International Conference on Fifth Generation
Computer Systems, June 1–5, 1992, Tokyo, Japan: IOS Press, 1992; Institute for New
Generation Computer Technology (ICOT, ed.), Proceedings of the International Conference
on Fifth Generation Computer Systems, November 28–December 2, 1988, 3 volumes, Tokyo,
Japan: OHMSHA, Ltd., and Berlin: Springer-Verlag, 1988; Institute for New Generation
Computer Technology (ICOT, ed.), Proceedings of the International Conference on Fifth
Generation Computer Systems, November 6–9, 1984, Tokyo, Japan: OHMSHA, Ltd., and
Amsterdam: North-Holland, 1984. [351]
5. For a Web page describing the various PIMs, see
http://www.icot.or.jp/ARCHIVE/Museum/MACHINE/pim-spec-E.html. [351]
6. See, for example, Ryuzo Hasegawa, Miyuki Koshimura, and Hiroshi Fujita, “MGTP: A
Parallel Theorem Prover Based on Lazy Model Generation,” Automated Deduction –
CADE-11, Lecture Notes in Computer Science, Proceedings of the 11th International
Conference on Automated Deduction, Berlin/Heidelberg: Springer-Verlag, 1992. [351]
7. See, for example, Shinichi Honiden, Akihiko Ohsuga, and Naoshi Uchihira, “MENDELS
ZONE: A Parallel Program Development System Based on Formal Specifications,”
Information and Software Technology, Vol. 38, No. 3, pp. 181–189, March 1996. [351]
8. See, for example, Katsumi Nitta et al., “HELIC-II: Legal Reasoning System on the
Parallel Inference Machine,” New Generation Computing, Vol. 11, Nos. 3–4, pp. 423–448,
July 1993. [351]
9. Kazuhiro Fuchi et al., “Launching the New Era,” Communications of the ACM, Vol. 36,
No. 3, pp. 49–100, March 1993. [352]
10. Ibid, p. 99. [352]
11. See, for example, T. Motooka et al., “Challenge for Knowledge Information Processing
Systems (Preliminary Report on FGCS),” Proceedings of the International Conference on
FGCS, JIPDEC, pp. 1–85, 1981. [353]
12. For a history of the first ten years or so of MCC, see David V. Gibson and Everett M.
Rogers, R & D Collaboration on Trial: The Microelectronics and Computer Technology
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![22 NOTES
Corporation, Cambridge, MA: Harvard Business School Press, 1994. [354]
13. The committee’s report, from which these quotations are taken, is available online from
pointers at
http://www.chilton-computing.org.uk/inf/literature/reports/alvey report/p001.htm. [355]
14. Brian W. Oakley, “Intelligent Knowledge-Based Systems – AI in the U.K.,” in Ray
Kurzweil, The Age of Intelligent Machines, Cambridge, MA: MIT Press, 1990. Available
online at http://www.kurzweilai.net/articles/art0308.html?printable=1. [355]
15. Brian Oakley and Kenneth Owen, Alvey: Britain’s Strategic Computing Initiative,
Cambridge, MA: MIT Press, 1990. [355]
16. Luc Steels and Brice Lepape, “Knowledge Engineering in ESPRIT,” IEEE Expert, Vol.
8, No. 4, pp. 4–10, August 1993. [356]
17. At this writing, there are still abundant Web pages about ESPRIT. They are available
from http://cordis.europa.eu/esprit/home.html. [356]
18. Luc Steels and Brice Lepape, op. cit. [356]
358
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![23 DARPA’s Strategic Computing Program
Figure 23.1: Robert E. Kahn. (Photograph courtesy of Robert E. Kahn.)
Kahn thought that AI, especially expert systems, could play a major role
in SC. Recall that in the mid-1970s DARPA support for AI research suffered
during George Heilmeier’s tenure as the DARPA Director. A major casualty
was the speech understanding program. The SC program could revitalize AI
research, but more importantly in Kahn’s view, it would help transfer
promising AI techniques out of university laboratories and into actual
applications. Alex Roland, who wrote a well-researched book about the
history of the SC program, put it this way:2
Robert Kahn and the architects of SC believed in 1983 [after the
expert systems boom] that AI was ripe for exploitation. It was
finally moving out of the laboratory and into the real world. . . AI
would become an essential component of SC; expert systems would
be the centerpiece. [They] would allow machines to “think.”
Kahn saw the SC program as a pyramid of related technologies to be
developed. At the base were enabling technologies such as facilities for rapid
360
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![23 DARPA’s Strategic Computing Program
Congressional committees overseeing DARPA’s budget. Cooper knew that
emphasizing (indeed, promising) specific military applications was how to sell
Congress. He was right as far as convincing Congress was concerned, but in
the end technology pull didn’t work so well.
One factor in helping to convince Congress about the need for the SC
program was the Japanese FGCS program. According to Roland “Congress
was more exercised by Japan’s Fifth Generation program than either the
Reagan administration or the computer community [including Kahn and
Cooper].”4
The publication of a book5
about the Japanese project by Edward
Feigenbaum and Pamela McCorduck had the effect of strengthening these
concerns. In the preface to their book, they asked “Will we rise to [this crucial
challenge]? If not, we may consign our nation to the role of the first great
postindustrial agrarian society.” They further warned that6
. . . our national self-interest, not to mention our economic security,
does not allow us [to ignore the Japanese project]. Information
processing is an $88-billion-per-year industry in the United States,
and its loss would be disastrous. The default of this American
industry, which has led the world for decades, would be a mortal
economic wound. . . . The superior technology usually wins the war
– whether that war is martial, entrepreneurial, or cultural.
In June 1983, Feigenbaum testified before the House Committee on
Science, Space, and Technology. According to Roland he told the committee
“the era of reasoning machines is inevitable. . . It is the manifest destiny of
computing.”7
Kahn was persuaded to yield to Cooper’s vision about how to frame the
plan, and it was finally written up in October 1983.8
Funds to support SC
were approved at a level of $50 million for work to begin in fiscal year 1984.
(One Congressional staff person even recommended that DARPA spend “a
substantially higher amount.”) During the decade from 1983 to 1993 DARPA
spent just over $1 billion on SC.9
The plan envisioned supporting two main
thrusts, namely, major projects that would build specific applications and
basic research to develop the “technology base” that would be needed for those
applications. I will describe aspects of each of these in the following sections.
23.2 Major Projects
At the beginning of the program, three major applications were chosen. To get
all three of the military services to “buy in” to the program, the plan called
for a “Pilot’s Associate” (for the Air Force), an aircraft carrier “Battle
Management System” (for the Navy), and an “Autonomous Land Vehicle” (for
the Army). However, to encourage joint service support of all of these, the
plan cautioned that10
362
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![23.2 Major Projects
[i]t might, for example, prove preferable to pursue an autonomous
underwater vehicle rather than a land vehicle, and a battle
management system for land combat might prove more appropriate
than that for the Naval application.
As Mark Stefik wrote in his excellent review and assessment of the
program, “. . . DARPA is telling the services that the particulars of these
applications can be shuffled at any time, so they had better buy the whole
plan.”11
Indeed, additional applications, such as an “Autonomous Air Vehicle”
for launching smart bombs behind enemy lines, were undertaken later.
23.2.1 The Pilot’s Associate
The Pilot’s Associate (PA) program, begun in February 1986, had as its goal
the development of an interactive computer system that would aid a combat
aircraft commander. Interaction with the system would be through a graphics
user interface, voice recognition (capable of working under noisy and stressful
conditions), and speech synthesis. It would prepare and revise mission plans,
advise the pilot about targets, assess threats, help the pilot to take evasive
action against interceptor missiles (flying the plane automatically in case it
needed to maneuver so rapidly that the pilot might black out), and take over
routine tasks. In addition to pilot inputs, the system would obtain information
from navigational aids and several sensors. Advice and decisions would be
based on several collaborating expert systems, automatic planning systems,
and plan-execution systems.
Among the technical capabilities that the 1983 DARPA strategic plan
predicted could be achieved by 1989 were 10,000-rule, real-time expert
systems, animated displays with 108
polygons per second, 200-word,
speaker-independent speech recognition in high-noise environments, and a
speech output system capable of a 1,000-word vocabulary.12
Of course,
compact and aircraft-worthy hardware would be required also.
The program was administered by the Avionics Laboratory at
Wright-Patterson Air Force Base in Dayton, Ohio (later part of the Air Force
Research Laboratory).13
After a preliminary exploratory effort (in which five
contractors participated), teams led by Lockheed Aeronautical Systems (later
part of Lockheed Martin Corporation) and McDonnell Douglas (later part of
Boeing) were awarded contracts for work extending from 1986 through the
middle of 1992. As had become standard for DARPA-managed projects,
working demonstrations had to be given. According to a set of Web sites (last
updated in 2004) describing the project, the Lockheed program was awarded
the American Institute of Aeronautics and Astronautics (AIAA) Digital
Electronics Award “. . . in recognition of outstanding achievement
in. . . advancing the state-of-the-art of artificial intelligence and decision
support systems into the complex, rapidly changing world of air combat.”14
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![23 DARPA’s Strategic Computing Program
The same site also claimed the following:
Technically, by 1991, the PA was the most advanced, working,
real-time intelligent system of its day and remains unsurpassed in
the world. We [at Lockheed] successfully integrated 6 expert
systems operating in real time in a realistic (some would say too
realistic) combat simulator. The knowledge implemented in each
component of this system was realistic, combat experience that was
demonstrably applicable to the operation of combat aircraft today.
In spite of the successful demonstrations, the Defense Department did not
follow up directly in installing the technology in fighter aircraft. As one of the
Web sites just cited puts it, “. . . it was left to the PA contractors to take the
technology to the marketplace.”15
One of the legacies of the Pilot’s Associate
program was a system that was developed for use in Army helicopters, the
Rotorcraft Pilot’s Associate. A possible application involving air traffic
management never materialized even though “results [of NASA studies] were
enough to show that PA technology could enable free flight throughout the
continental and trans-oceanic air space, with enormous savings in operational
costs for the airlines and the Air Traffic Control infrastructure, while also
offering an extra level of safety.”16
23.2.2 Battle Management Systems
In 1984 DARPA began funding the Fleet Command Center Battle
Management Program (FCCBMP, pronounced “fik bump”). A company called
Analytics was the engineering contractor for FCCBMP. It provided program
management support, testing, and configuration management. In a 1990
article describing and assessing the program, Rin Saunders, an engineer at
Analytics, wrote that its goal was to produce a system that would “. . . assist
the commander-in-chief of the U.S. Pacific Fleet (CINCPACFLT) in planning
and monitoring the operation of nearly 300 ships in the Pacific and Indian
ocean regions.”17
Saunders claimed that it was the most successful of the
Strategic Computing programs in bringing expert systems into operational use
and had “the greatest visibility and participation within the user community.”
Expert systems were planned to play a major role in FCCBMP. The
DARPA strategic plan envisioned ones that could process 10,000 rules per
second in highly complex contexts operating at five times real time.18
FCCBMP consisted of two major expert systems communicating over a local
area network. One of these, the “Force Requirements Expert System
(FRESH),” was designed to keep track of the current positions and the
readiness status of ships in the fleet and to issue alerts when there were
significant changes. FRESH, developed by Texas Instruments, was supposed to
be able to make suggestions about what should be done, such as proceeding
364
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![23.2 Major Projects
with the current plan anyway, expediting repairs, or substituting other ships
for unready ones.
Saunders claimed that19
FRESH currently provides daily alert summaries to twelve
CINCPACFLT staff codes. FRESH was shown to replicate expert
judgement in a trial in which FRESH and CINCPACFLT staff were
given the same nomination task to solve. CINCPACFLT staff
estimate that FRESH can accomplish in one minute monitoring
tasks which previous[ly] took two hours, yielding a 120-fold time
savings. For the planning task of nominating a replacement for a
disabled ship, the time savings is 400 to 1.
Another expert system, the “Capabilities Assessment Expert System”
(CASES), used information about U.S. and enemy forces to provide estimates
of how each would fare in hypothetical engagements. It was developed by
BBN. According to Saunders,20
CASES has been used to evaluate carrier battle force operating
areas; assess attack submarine employment strategies; estimate the
effect of pre-D-day surveillance and early assignment of SSNs to
enemy submarine attrition; and provide insights on the costs and
benefits of different strike strategies relative to changes in
estimated enemy capability and weather.
Both expert systems made extensive use of natural language
understanding and generation abilities. They were hosted on Symbolics Lisp
machines and written using commercial expert-system “shells.” The battle
simulations in CASES were run on an Encore parallel processor. The final
prototypes for FRESH and CASES were delivered to CINCPACFLT in 1990.
But when the prototype phase was complete and DARPA funding ended, the
Navy decided not to continue these systems.
Saunders, now a Technical Director at Computer Sciences Corporation,
has provided me with some recollections about FCCBMP. These contrast a bit
with what he wrote in 1990. In e-mail notes, he wrote me that21
[t]he goals of FCCBMP were an overreach for the state of the art
in the 1980s.
. . .
[The] Navy’s decision to mothball FRESH was because there was no
compelling reason to keep it. It duplicated the expert judgement of
Fleet planners, in a matter of hours rather than days. But the
planners were not looking to retire, and in Naval warfare, days are
good enough.
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![23.3 AI Technology Base
its speech understanding research program in 1976, but continuing progress in
the field, notably at CMU, MIT, and IBM, justified its resumption.
Texas Instruments integrated work from eight speech recognition research
projects for the Pilot’s Associate project. CMU integrated work from nine
projects for the Battle Management projects. According to Roland, by the end
of the SC program, speech recognition systems “could recognize 10,000 words
of natural language spoken by anyone in an environment of moderate
background noise and low stress on the speaker.”34
Later, at CMU, Kai-Fu Lee and others developed the speaker-independent
SPHINX speech recognition system under DARPA support.35
SPHINX used
HMMs (hidden Markov models) and statistical information about the
likelihoods of word strings to aid recognition.
Speech recognition work continues at several industrial and university
research laboratories. At CMU, to name just one example, the SPHINX project
has assembled “a set of reasonably mature [open source], world-class speech
components that provide a basic level of technology to anyone interested in
creating speech-using applications.” However, the project Web page at
SourceForge36
cautions that “. . . SPHINX is not a final product. Those with a
certain level of expertise can achieve great results with the versions of SPHINX
available here, but a naive user will certainly need further help. In other
words, the software available here is not meant for users with no experience in
speech, but for expert users.” Advances in computer speed and memory have
led to several high-quality, real-time, and commercially available speech
recognition systems of moderate cost.
The SC program also sponsored basic research work on text
understanding as part of what DARPA called “new generation systems.”
Seven contractors received DARPA awards in 1984.37
BBN Laboratories,
USC/Information Sciences Institute, the University of Pennsylvania, and the
University of Massachusetts worked on natural language interfaces that could
respond to typed queries. New York University, the Systems Development
Corporation (later to become part of Unisys), and SRI International worked
on understanding free-form text from military messages. BBN and USC–ISI
coordinated the work on interfaces based initially on BBN’s IRUS system,
which later grew into JANUS.38
New York University and SDC coordinated the
work on text understanding and developed the PROTEUS and PUNDIT
systems.39
According to Roland, the results of this work “were most promising, again
exceeding the milestones laid out in the original SC plan. . . . the metrics of the
original plan [were] exceeded in error rate and [in] the size of vocabularies.”40
Roland goes on to elaborate:41
. . . BBN’s IRUS system, with a vocabulary of 4,500 words and 1,200
domain concepts, received a favorable response in 1986 when it was
installed in a Battle Management Program test bed. By 1987 BBN
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![23.4 Assessment
knowledge-based systems (KBSs), Frederick Hayes-Roth and Neil Jacobstein,
two pioneers in expert system applications, wrote “. . . although the current
generation of expert and KBS technologies had no hope of producing a robust
and general human-like intelligence, many people were disappointed that it did
not.”48
Even so, Hayes-Roth and Jacobstein concluded the following:49
KBS have been remarkably effective over the past decade of
industrial experience – often delivering order-of-magnitude
increases in speed, quality, or cost performance. They have
penetrated every major institution from Fortune 500 companies to
small entrepreneurial firms, military services, government agencies,
health care, and educational institutions. KBS applications may
now be found in virtually every field of human endeavor from
music to medicine.
DART, an acronym for Dynamic Analysis and Replanning Tool, was a
KBS whose roots can be traced to AI research during the SC program.
Developed at BBN, it helped plan the movement of equipment and personnel
from Europe to Saudi Arabia during the 1990 Persian Gulf War. In fact,
Victor Reis, the Director of DARPA at the time, has been quoted as claiming
“The DART scheduling application paid back all of DARPAs 30 years of
investment in AI in a matter of a few months.”50
23.4 Assessment
What can be said about the SC program overall? It has been criticized both
by those who thought it might achieve its military goals and also by those who
faulted it for not having done so. Among the former, the Computer
Professionals for Social Responsibility (CPSR) had several concerns, stated in
one of their newsletters:51
1. The SCI [Strategic Computing Initiative] promises specific new
weapons systems; autonomous vehicles, such as robot tanks; a
combat pilot’s “associate;” and an aircraft carrier group “battle
management system.” Our concern is that proposals for computer
research will be assessed by their relevance to these specific
applications, rather than by their general scientific merit.
2. The SCI promotes the use of machine “intelligence,” to control
the operation of complex military systems under unpredictable
circumstances. Our concern is that, particularly when the stakes
are high, situations of extreme uncertainty are precisely the wrong
environment for the application of artificial intelligence.
3. The SCI promotes the military application of computer
technology as a solution to perceived problems in defense. Our
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![23.4 NOTES
taking on a wide variety of real-world applications. As I’ll describe in a
subsequent chapter, much new AI technology was invented during the 1980s
and 1990s, and some of it at least was stimulated by the new-generation
projects. However, before continuing with my roughly chronological history,
I’ll make a temporary diversion to discuss controversies that were simmering
on the sidelines and within the field itself.
Notes
1. To learn more about the evolution of the Internet, see Robert E. Kahn and Vinton G.
Cerf, “What Is the Internet (And What Makes It Work)” available online at
http://www.cnri.reston.va.us/what is internet.html. [359]
2. Alex Roland (with Philip Shiman), Strategic Computing: DARPA and the Quest for
Machine Intelligence, 1983–1993, pp. 191–192, Cambridge, MA: MIT Press, 2002. [360]
3. Ibid, p. 71. [361]
4. Ibid, p. 91. [362]
5. Edward Feigenbaum and Pamela McCorduck, The Fifth Generation: Japan’s Computer
Challenge to the World, Boston, MA: Addison-Wesley Longman Publishing Co., Inc., 1983.
An article with the same title by the same authors appeared in Creative Computing, Vol. 10,
No. 8, p. 103, August 1984, and is available online at
http://www.atarimagazines.com/creative/v10n8/103 The fifth generation Jap.php. [362]
6. Edward Feigenbaum and Pamela McCorduck, op. cit., pp. 19–20. [362]
7. In Alex Roland, op. cit., pp. 91–92. Roland cites U.S. Congress, House, Committee on
Science, Space, and Technology, Japanese Technological Advances and Possible U.S.
Responses Using Research Joint Ventures. Hearings, 98th Congress, 1st session, June,
29–30, 1983, pp. 116–143, at p. 119. [362]
8. “Strategic Computing: New-Generation Computing Technology: A Strategic Plan for
Its Development and Applications to Critical Problems in Defense,” Defense Advanced
Research Projects Agency, Arlington, Virginia, October 28, 1983. [362]
9. Alex Roland, op. cit., p. 319. [362]
10. “Strategic Computing: New-Generation Computing Technology: A Strategic Plan for its
Development and Applications to Critical Problems in Defense,” p. 20, Defense Advanced
Research Projects Agency, Arlington, Virginia, October 28, 1983.. [362]
11. Mark Stefik, “Strategic Computing at DARPA: An Overview and Assessment,”
Communications of the ACM, Vol. 28, No. 7, pp. 690–704, July 1985. [363]
12. “Strategic Computing: New-Generation Computing Technology: A Strategic Plan for
Its Development and Applications to Critical Problems in Defense,” Chart I.2 of appendix I,
Defense Advanced Research Projects Agency, Arlington, Virginia, October 28, 1983. [363]
13. For a description and review of the program by people from Wright-Patterson Air Force
Base see Sheila B. Banks and Carl S. Lizza, “Pilot’s Associate: A Cooperative,
Knowledge-Based System Application,”IEEE Expert, Vol. 6, No. 3, pp. 18–29, 1991. [363]
14. http://www.dms489.com/PA/PA index.html. [363]
15. http://www.dms489.com/PA/PA index.html. [364]
16. http://www.dms489.com/PA/PA index.html. [364]
17. Rin Saunders, “The Fleet Command Center Battle Management Project: Lessons
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![23 NOTES
Learned,” Proceedings of the IEEE Conference on Managing Expert System Programs and
Projects, pp. 51–60, September 1990. [364]
18. “Strategic Computing: New-Generation Computing Technology: A Strategic Plan for its
Development and Applications to Critical Problems in Defense,” Chart I.3 of Appendix I,
Defense Advanced Research Projects Agency, Arlington, Virginia, October 28, 1983. [364]
19. Rin Saunders, op. cit., p. 53. [365]
20. Ibid, p. 53. [365]
21. E-mail correspondence of December 12, 2007. [365]
22. Alex Roland, op. cit., p. 222. [366]
23. Douglas W. Gage, “UGV HISTORY 101: A Brief History of Unmanned Ground Vehicle
(UGV) Development Efforts,” Unmanned Systems Magazine, Special Issue on Unmanned
Ground Vehicles, Vol. 13, No. 3, Summer 1995. [366]
24. “Strategic Computing: New-Generation Computing Technology: A Strategic Plan for
Its Development and Applications to Critical Problems in Defense,” Defense Advanced
Research Projects Agency, Arlington, Virginia, October 28, 1983. The milestones described
in this paragraph, along with many others, are taken from Chart I.1 in Appendix I. [368]
25. National Research Council Staff, Technology Development for Army Unmanned Ground
Vehicles, pp. 152–153, Washington, DC: National Academies Press, 2003. [368]
26. Alex Roland, op. cit.. [368]
27. National Research Council Staff, Technology Development for Army Unmanned Ground
Vehicles, p. 148, Washington, DC: National Academies Press, 2003. [368]
28. Alex Roland op. cit.. [368]
29. For summaries of DEMO-I and DEMO-II technology and demonstrations, see Douglas
W. Gage, “UGV HISTORY 101: A Brief History of Unmanned Ground Vehicle (UGV)
Development Efforts,” Unmanned Systems Magazine, Special Issue on Unmanned Ground
Vehicles, Vol. 13, No. 3, Summer 1995, and National Research Council Staff, Technology
Development for Army Unmanned Ground Vehicles, p. 148, Washington, DC: National
Academies Press, 2003. The latter book recommends Oscar Firschein and Thomas Strat
(eds.), Reconnaissance, Surveillance, and Target Acquisition for the Unmanned Ground
Vehicle: Providing Surveillance “Eyes” for an Autonomous Vehicle, San Francisco, CA:
Morgan Kaufmann Publishers, 1997. [369]
30. See, for example, Robert L. Simpson Jr., “Computer Vision: An Overview,” Guest
Editor’s Introduction, IEEE Expert, pp. 11–15, August 1991. [370]
31. See, for example, Takeo Kanade, Chuck Thorpe, and William Whittaker, “Autonomous
Land Vehicle Project at CMU,” Proceedings of the 1986 ACM Computer Conference, pp.
71–80, February 1986, and Yoshimasa Goto and Anthony Stentz, “Mobile Robot Navigation:
The CMU System,” IEEE Expert, pp. 44–54. 1987. (The latter paper is available online at
http://www.ri.cmu.edu/pub files/pub3/goto y 1987 1/goto y 1987 1.pdf.) [370]
32. Personal communication, November 15, 2007. [370]
33. See Thomas M. Strat and Oscar Firschein, RADIUS: Image Understanding for Imagery
Intelligence, San Francisco: Morgan Kaufmann Publishers, 1997. See also the RADIUS Web
site at http://www.ai.sri.com/∼radius/. [370]
34. Alex Roland, op. cit. For this information, Roland cites Victor Zue, a speech researcher
and professor at MIT. [371]
35. Kai-Fu Lee, Hsiao-Wuen Hon, and Raj Reddy, “An Overview of the SPHINX Speech
Recognition System,” IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol.
38, No. 1, January 1990. Available online at
http://www.ri.cmu.edu/pub files/pub2/lee k f 1990 1/lee k f 1990 1.pdf. [371]
376
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![23.4 NOTES
36. http://cmusphinx.sourceforge.net/html/cmusphinx.php. [371]
37. Proceedings of the Workshop on Strategic Computing Natural Language, Foreword by
Robert Simpson, Morristown, NJ: Association for Computational Linguistics, 1986. [371]
38. For a brief history of the IRUS/JANUS work (as well as related work at BBN), see
Ralph Weischedel, “Natural-Language Understanding at BBN,” IEEE Annals of the History
of Computing, pp. 46–55, January–March 2006. A BBN report about the project is available
online a http://www.aclweb.org/anthology-new/H/H86/H86-1001.pdf. [371]
39. See the online document by Ralph Grishman and Lynette Hirschman, “PROTEUS and
PUNDIT: Research in Text Understanding,” available at
http://www.aclweb.org/anthology-new/H/H86/H86-1002.pdf. [371]
40. Alex Roland, op. cit., p. 212. [371]
41. Ibid, pp. 269–270. [371]
42. Ibid, p. 195. [372]
43. T. P. Kehler and G. D. Clemenson, “KEE – The Knowledge Engineering Environment
for Industry,” Systems and Software, Vol. 3, No. 1, pp. 212–224, January 1984. [372]
44. Alex Roland, op. cit. p. 198. [372]
45. Richard Fikes et al., “OPUS: A New Generation Knowledge Engineering Environment,”
Phase 1 Final Report, lntelliCorp, Mountain View, CA, 1987. [372]
46. Lee D. Erman, Jay S. Lark, and Frederick Hayes-Roth, “ABE: An Environment for
Engineering Intelligent Systems,” IEEE Transactions on Software Engineering, Vol. 14, No.
12, pp. 1758–1770, December 1988. [372]
47. Alex Roland, op. cit., p. 201. [372]
48. Frederick Hayes-Roth and Neil Jacobstein, “The State of Knowledge-Based Systems,”
Communications of the ACM, Vol. 37, No. 3, p. 36, March 1994. [373]
49. Ibid, p. 36. [373]
50. Sara Reese Hedberg, “DART: Revolutionizing Logistics Planning,” IEEE Intelligent
Systems, p. 81, May/June 2002. [373]
51. The CPSR Newsletter, Vol. 2, No. 2, Spring 1984. Also see S. M. Ornstein, B. C.
Smith, and L. A. Suchman, “Strategic Computing: An Assessment,” Bulletin of the Atomic
Scientists, Vol. 40, No. 10, pp. 11–15, December 1984. [373]
52. Alex Roland, op. cit., p. 243. [374]
53. Ibid, p. 285. [374]
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![24.1 Opinions from Various Onlookers
Figure 24.2: John Searle. (Photograph courtesy of John Searle.)
Searle imagines that his room contains two batches of Chinese symbols, which,
unknown to Searle, constitute a story and general background information
about such stories. The room also contains rules, written in English, about
how to manipulate sets of Chinese characters and how to generate Chinese
characters as a result of such manipulations.
Into such a room, then, comes a third batch of Chinese symbols. As
Searle puts it, he has rules in the room (written in English) that
instruct me how to give back certain Chinese symbols with certain
sorts of shapes in response to certain sorts of shapes given me in
the third batch. Unknown to me, the people who are giving me all
of these symbols call [the first two batches a story and its
background information] and they call the third batch “questions.”
Furthermore, they call the symbols I give them back in response to
the third batch “answers to the questions,” and the set of rules in
English that they gave me, they call the “program.” . . . Suppose
also that after a while I get so good at following the instructions
for manipulating the Chinese symbols and the programmers get so
good at writing the programs that from the external point of view
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![24 Speed Bumps
a world of objects wider than just these symbolic expressions
themselves.
Regarding this “world of objects,” a physical symbol system includes (in
addition to its means for formal symbol manipulation) the ability to
“designate.” Here is Newell and Simon’s definition: “An expression [composed
of symbols] designates an object if, given the expression, the system can either
affect the object itself or behave in ways dependent on the object ” (my italics).
So, I believe Searle is simply confused about some basic ideas in computer
science. Although it has not yet been empirically established that a computer
(manipulating symbols and attached as needed to its environment) can be
made to exhibit all of the aspects of intelligent behavior of which humans are
capable, I don’t believe that Searle’s thought experiment casts doubt on the
possibility.
Searle himself believes that physical systems of some sort can be
intelligent and understand things. He believes that humans are one kind of
such a system. He wrote as follows:
“Could a machine think?”
The answer is, obviously, yes. We are precisely such machines.
“Yes, but could an artifact, a man-made machine think?”
Assuming it is possible to produce artificially a machine with a
nervous system, neurons with axons and dendrites, and all the rest
of it, sufficiently like ours, again the answer to the question seems
to be obviously, yes.
Yet, Searle gives us no clue as to what it is about brains, composed of
neurons, that is different from computers, composed of transistors, that
endows the former, but not the latter, with intentionality. He claims that for a
machine to think it would have to have “internal causal powers” equivalent to
those of brains. He does not say just what these internal causal powers might
be.
C. Strong and Weak AI
Searle’s paper introduced definitions for “strong AI” and “weak AI” that are
useful for distinguishing between two types of AI endeavors. Strong AI is
associated with the claim that an appropriately programmed computer could
be a mind and could think at least as well as humans do. Achieving strong AI
is the ultimate goal for many artificial intelligence researchers. Searle’s article
attempts to show that strong AI (using computers) is impossible. However,
practitioners of weak (or “cautious”) AI use programs as a tool to study the
mind by formulating and testing hypotheses about it. Weak AI has also come
388
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![24 Speed Bumps
An overall pattern is taking shape: an early, dramatic success
based on the easy performance of simple tasks, or low-quality work
on complex tasks, and then diminishing returns, disenchantment,
and, in some cases, pessimism.
A typical case, he claimed, was Gelernter’s geometry-theorem proving
machine: “No more striking example exists of an ‘astonishing’ early success
and the equally astonishing failure to follow it up.” And, answering AI’s claim
that progress is being made, he wrote “According to this definition [of
progress], the first man to climb a tree could claim tangible progress toward
flight to the moon.”
One of the reasons for this stagnation, according to Dreyfus, was that AI
research is based on the assumption that thinking can be analyzed as a finite
set of simple determinate operations (such as the application of rules to a finite
set of data). Rather, he claimed, “thinking involves global processes, which
cannot be understood in terms of a sequence or even a parallel set of discrete
steps.” These global processes are manifest in three ways. The first is “fringe
consciousness.” It is what the brain uses to access the infinite “open-ended
information characteristic of everyday experience.” Fringe consciousness allows
humans to consider details and the big picture simultaneously. Another global
process is at work in human thinking when we distinguish the essential from
the unessential. The third is “global context,” which allows us to reduce
ambiguity. A combination of these abilities permits what he calls “perspicuous
grouping” – what the brain does when it recognizes complex patterns, such as
human faces, for example. Dreyfus claimed that computer programs are unable
to employ these global processes, which are essential for intelligent behavior.
Dreyfus stated that the brain processes information in an entirely
different way than a computer does. He wrote that information in the brain is
“processed globally the way a resistor analogue [a kind of analog computer]
solves the problem of the minimal path through a network.” Furthermore, he
said that the “body plays a crucial role in making possible intelligent
behavior.” Several other people have emphasized the importance of
“embodiment” for progress in AI, and I’ll have more to say about that topic
shortly.
About the future (as judged from the early 1960s), Dreyfus wrote
Only experimentation can determine the extent to which newer
and faster machines, better programming languages, and clever
heuristics can continue to push back the frontier. Nonetheless, the
dramatic slowdown in the fields we have considered and the general
failure to fulfill earlier predictions suggest the boundary may be
near.
Dreyfus’s comments on AI should not be taken to imply that he thought
that human-level artificial intelligence by machines is impossible – he just
390
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![24.1 Opinions from Various Onlookers
Figure 24.4: Joseph Weizenbaum. (Photograph courtesy MIT Museum.)
obscenity. That it has to be put in print at all, even for the purpose
of exposing its morbidity, is a sign of the madness of our times.
. . .
[The relevant issues] cannot be settled by asking questions
beginning with “can.” The limits of the applicability of computers
are ultimately statable only in terms of oughts. What emerges as
the most elementary insight is that, since we do not now have any
ways of making computers wise, we ought not now to give
computers tasks that demand wisdom.
Even though Weizenbaum hedges a bit on the “can” question, I believe he
really believed that machines “could not” as well as “should not.” For if
machines really could make judgments with all of the “compassion and
wisdom” with which humans can, why shouldn’t they? In addition to the
concern about using any technology for antisocial purposes (such as war), the
real danger, I think, lies in the premature use of machines: thinking that they
are able to perform a task before they are really competent to do so.
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![24 Speed Bumps
Another person who recoiled from the prospect of machines “taking over”
is the physician, biologist, and essayist Lewis Thomas (1913–1993). In one of
his celebrated columns, “Notes of a Biology-Watcher,” in The New England
Journal of Medicine, he wrote29
The most profoundly depressing of all ideas about the future of the
human species is the concept of artificial intelligence. The ambition
that human beings will ultimately cap their success as evolutionary
overachievers by manufacturing computers of such complexity and
ingenuity as to be smarter than they are, and that these devices
will take over and run the place for human betterment or perhaps,
later on, for machine betterment, strikes me as wrong in a deep
sense, maybe even evil.
. . .
This is what the artificial intelligence people are talking about: a
mechanical brain with the capacity to look back over the past and
make accurate predictions about the future, then to lay out flawless
plans for changing that future any way it feels like, and, most
appalling of all, capable of feeling like doing one thing or another.
. . .
It is, in my view, an absolutely hideous prospect, and if I thought
it were really something waiting ineluctably ahead of us I would
spend all my days in protest.
Although there have been several other authors who have warned about
the dangers of the inappropriate use of computers in general and of intelligent
machines in particular, I’ll mention just one more, a self-confessed
“neo-Luddite.” Theodore Roszak (1933– ) is a prominent author and social
thinker – one well worth reading in my opinion. In his book The Cult of
Information,30
he claimed that a growing cult, infatuated with “information”
and “information processing,” is having debilitating cultural effects –
“especially when it comes to teaching the young.” Roszak wrote that he is “an
ally of all those serious students and users of information technology who hold
a reasonably balanced view of what computers can and cannot, should and
should not, do,” but claims that “the creation of a mystique of information
[has made] basic intellectual discriminations between data, knowledge,
judgment, imagination, insight and wisdom impossible.”31
Claiming that there is a “vital distinction” between information
processing and thinking, he wrote32
Because the ability [of the computer] to store data somewhat
corresponds to what we call memory in human beings, and because
the ability to follow logical procedures somewhat corresponds to
what we call reasoning in human beings, many members of the cult
396
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![24.1 Opinions from Various Onlookers
[of information] have concluded that what computers do somewhat
corresponds to what we call thinking.
However, Roszak concludes, computers cannot really “think.” The danger
is that those who are persuaded (or duped into believing) that they can might
inappropriately employ computers in tasks that require thinking and not just
“data processing.” I believe Roszak has a legitimate concern here – AI is not
yet up to all of the tasks to which we might try to put it.
But Roszak also wrote33
There is no possibility that computers will ever equal or replace the
mind except in those limited functional applications that do
involve data processing and procedural thinking. The possibility is
ruled out in principle, because the metaphysical assumptions that
underlie the effort are false.
Here, I disagree. I know of no “metaphysical assumptions” of AI other
than that the brain is a kind of machine and therefore we ought to be able to
understand it and build something that works very much like it. Furthermore,
I know of no credible evidence that that metaphysical assumption is false.
Although he does not think that computers can become minds, he worries
about the additional danger that “it is possible to redefine the mind and its
uses in ways that can be imitated by machine. Then we have a mechanical
caricature which levels the activity down to a lower standard.”34
Roszak does have at least two good things to say about AI – one a
negative result and one a positive contribution. As for the negative result, he
says35
There is an ironic but highly valuable quality to AI in all its forms.
The effort to simulate or surpass human intelligence is uncovering
subtleties and paradoxes about the human mind we might never
have imagined. By way of heroic failures, AI is teaching us how
truly strange real intelligence is.
On the positive side he comments that36
One field of AI, however, has made remarkable progress. . . Often,
by quizzing specialists closely about their work, computer
programmers can tease out procedures, assumptions, values that
can then be formally specified. The result is an Expert System, one
of the few practical applications of AI. Edward Feigenbaum sees
such systems as the gateway to the next era of machine
intelligence; he calls it “knowledge processing,” as opposed to mere
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![24.2 Problems of Scale
Of course, even early AI researchers knew about combinatorial explosions.
That’s the reason for their interest in heuristics. Unfortunately, heuristics do
not change the exponential character of search – at best they reduce the
branching factor. The “explosion” still happens – just not quite so rapidly.
For example, reducing the branching factor from 10 to 4 and searching again
to depth 20 would still require the generation of over 420
or, roughly, one
trillion nodes.
AI critics have focused on this problem in their pessimistic assessments of
AI’s achievements and prospects. For example, Sir James Lighthill (in his
report that I mentioned in Chapter 16) wrote that “one rather general cause
for the [AI] disappointments that have been experienced [is a] failure to
recognize the implications of the ‘combinatorial explosion.’” Lighthill’s report
did cause, as I have already mentioned, funding difficulties for AI research in
Britain.
24.2.2 Complexity Theory
AI researchers are not the only people who are concerned about the
computational difficulty of problem solving. A branch of computer science
called “complexity theory” deals with how long and how much storage space
different kinds of programs might take (in the worst case) to solve different
kinds of problems. They ask, among other things, “How does the size of a
problem affect the time and space required to solve it?”
Let’s look at some examples. The time that it would take for a computer
program to find the largest number in a list of numbers is proportional to the
size of this problem, namely, the number of items in the list. (The worst case
would happen if the largest item happened to be the last item in the list; a
program would then have to examine each item in the list.) Such a program is
said to take “linear time.” Similarly, a program for finding out whether a given
item is a member of a list of items would take linear time. (Again, the worst
case would happen if the item happened to be the last item in the list.) In
both cases, if we were to double the size, we would double the time required.
Sorting a list of names, putting them in alphabetical order for example, is
a harder problem. Programs for sorting lists differ in how long they take and
how complicated they are to program. Reasonably simple sorting programs
take time that is proportional to the square of the number of items in the list.
That is, for these programs, sorting a list that has 100 items in it would take
four times as long as a list with only 50 items in it. (Multiplying the number
of elements by two increases the sorting time by two squared, or four. Sorting
can actually be done faster. There are programs that can sort a list in time
proportional to the logarithm of the size times the size.) Programs that can (in
the worst case) take time proportional to a problem’s size, or the square, or the
cube, or other “powers” of the size, run in what is called “polynomial time.”
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![24 Speed Bumps
Problems themselves can be graded according to the complexity of the
least complex programs that are able to solve them. For example, if a program
exists that can solve a problem in linear time, but none could solve it faster,
than that problem is said to be of “linear complexity.” Finding out whether or
not an item is a member of a list of items is a problem of linear complexity.
We denote by P the class of problems that can be solved in polynomial time.
Members of this class include calculating the greatest common divisor and
determining whether or not a number is a prime number.
Unfortunately, as I have already mentioned, the search procedures used in
many AI programs require running times that are exponential in the size of
the problem. For example, searching a tree having branching factor of b to
depth d would take time proportional to bd
. Using my previous example, we
see that searching a tree with branching factor of 3 to a depth level of 10
requires the generation of 88,572 nodes. But doubling the depth to 20 would
require searching not four times 88,572 nodes (which would be the case if
search time was proportional to the square of the depth) but 88,572 × 88,572
or almost eight-billion nodes. Exponential complexity is much, much worse
than polynomial complexity!
The American computer scientist Stephen Cook (1939– ) [as well as the
Russian, and now American, computer scientist Leonid Levin (1948– )] made
major contributions to our understanding of the complexity of problems and
the programs used to solve them. In particular, Cook (and Levin
independently) defined the class called the nondeterministic polynomial, or
NP, class of problems. This is the class of problems for which a candidate
solution can be checked (but not necessarily solved) in polynomial time. For
example, a proposed sequence of moves to solve a sliding tile puzzle can be
checked to see whether it actually solves the puzzle in polynomial time, but (as
far as is known) it would require exponential time to find a solution. So sliding
tile problems, along with many other AI problems, belong to the class NP.39
It is not known whether or not there might be polynomial programs for
solving the problems in the class NP. If there were, NP would equal P. So far,
programs for solving problems in the class NP require exponential time (in the
worst case). Whether or not NP equals P is one of the most famous unsolved
problems in computer science. Many people think NP does not equal P
because otherwise we would have found out by now. (The Clay Mathematics
Institute has offered a prize of $1,000,000 for a solution of the P versus NP
question – that is, showing either that they are equal or not equal.)40
24.2.3 A Sober Assessment
These results in complexity theory caused some people to have grave doubts
about the prospects for artificial intelligence. One of the most penetrating,
and to my mind intelligent, assessments was written by the mathematician
and computer scientist Jacob T. Schwartz (1930–2009; Fig. 24.6), whom I
402
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![24.2 Problems of Scale
have already mentioned in connection with his tenure at DARPA. In his article
titled “The Limits of Artificial Intelligence,”41
Schwartz wrote that the
extraordinary powers of the human brain arose from the way it used its
prodigious computational and storage abilities “to organize information
presented in relatively disordered form into internally organized structures on
which sophisticated, coherent courses of symbolic and real-world action can be
based.” To rival the brain, what AI needs, and what AI has vainly been trying
to achieve, Schwartz claimed, are “coherent structures capable of directly
guiding some form of computer action. . . generated automatically from
relatively disorganized, fragmented input.” The ability to generate such
“organized structures” would constitute a tremendous breakthrough because
computers are “already enormously superhuman” in solving problems for
which they can “accept, retain, and utilize fully structured material.” “If the
basic obstacle posed by the need to program [computers] in detail could be
overcome,” he wrote, “computers could ingest the information contained in all
the world’s libraries and use this information with superhuman effectiveness.”
(Nowadays, of course, besides libraries there is all the information, and
misinformation, on the World Wide Web.)
Figure 24.6: Jacob (Jack) Schwartz. (Photograph used with permission of Diana
Schwartz.)
The methods that AI researchers had used for automatically creating
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![24 Speed Bumps
structure from fragmented input were all based on heuristic search – either
searching for chains of logical deductions or for paths in trees (and more
generally graphs). Logical deductions had been used (among other things) to
answer questions, to provide expert advice, and to generate programs and
plans. Searching for paths in graphs had been used to prove theorems in
geometry, to parse sentences, and to produce plans for robots. All of these are
instances of creating structure (to use Schwartz’s term) from unstructured
input. These “successes” however were all achieved on either trivially small
problems or on ones whose subject matter was strictly circumscribed.
Unfortunately, as Schwartz correctly claimed, all attempts to date to generate
“broadly useful symbolic structures from more disorganized and fragmentary
input” have invariably been defeated by the combinatorial explosion. He
summarized the situation by echoing Dreyfus’s charge that “. . . the history of
AI research to date [consisted] always of very limited success in particular
areas, followed immediately by failure to reach the broader goals at which
these initial successes seem at first to hint. . . ”
Schwartz’s opinions about AI did have consequential effects because, as I
mentioned in Section 23.4, he was the Director of the DARPA ISTO from 1987
to 1989 and presided over some cutbacks in AI research (including the
cancellation of one of my own research projects in 1987). Even though he was
generally dismissive of AI work, Schwartz did write that those “areas of AI to
which classical scientific and algorithmic techniques apply can be expected to
progress more rapidly than areas that deal with deeper problems for which
only less focused approaches are available.” As one example, he cited the
problem of determining “whether one or more objects of known shape moving
in an environment containing obstacles of other known shapes can pass from
one specified position to another without colliding either with the obstacle or
with each other.”
Although these results from complexity theory did constitute one of the
“speed bumps” in AI’s rapid forward progress, AI researchers quickly recovered
and found various ways around the combinatorial explosion problem. They
pointed out, for example, that complexity results were based on worst-case
performance, and solutions might often be found faster than in the worst case.
For example, I’ll mention the work of Richard Korf (1956– ; Fig. 24.7), an
AI researcher at the University of California at Los Angeles. Korf is well
known for his work on tackling extremely difficult search problems. He often
uses the sliding tile and other puzzles as laboratory “drosophila” for exploring
new ideas in search.
You will recall that I used the example of a sliding tile puzzle to illustrate
heuristic search processes. The one I used consisted of eight tiles in a 3 × 3
array; the classic version consists of fifteen tiles in a 4 × 4 array. One can
imagine larger versions, such as twenty-four tiles in a 5 × 5 array. The 4 × 4
puzzle already presents a quite challenging problem for heuristic search. In
fact, in his comments about the scaling difficulties of search processes, Jack
404
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![24.2 Problems of Scale
Figure 24.7: Richard Korf. (Photograph courtesy of Richard Korf.)
Schwartz wrote “. . . the graph of states [of the 3 × 3 puzzle] consists of 9!, or
362,880 [nodes], so even for so simple a problem brute-force graph search
begins to become taxing. For the corresponding 4 × 4 puzzle, whose state
space involves 16!, or over 1013
, nodes, it is completely infeasible.” [Actually,
Schwartz was off by (an inconsequential) factor of 2 in both cases. It has been
known since 1879 that if you start from any particular initial configuration,
you can only reach 1/2 of all the possible configurations.42
Thus, for the
eight-tile version, the entire state-space graph consists of two separate
disconnected graphs of size 9!/2 = 181, 440.]
Yet, not only has Korf written heuristic search programs to solve
instances of the 4 × 4 puzzle, but in an abstract of a 1996 paper he and his
co-author wrote 43
We have found the first optimal solutions to a complete set of
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![24 Speed Bumps
random instances of the Twenty-Four Puzzle, the 5 × 5 version of
the well-known sliding-tile puzzles. Our new contribution to this
problem is a more powerful admissible heuristic function.
. . .
[W]e observe that as heuristic search problems are scaled up, more
powerful heuristic functions become both necessary and
cost-effective.
The search space for the 5 × 5 puzzle is about a trillion times larger than that
of the 4 × 4 puzzle. It has about 7.756 ×1024
nodes.
Search is also made easier if one could be satisfied with “good” or
approximate solutions to problems rather than insisting on the “best”
solutions. In a subsequent chapter, I’ll describe some rather remarkable
progress on dealing with large search problems; this progress has flattened the
speed bumps a bit.
Even with these ways around the complexity results, however, AI people
did begin to acknowledge other shortcomings – a subject I’ll turn to next.
24.3 Acknowledged Shortcomings
As AI researchers began to confront problems of practical importance, they
themselves had to acknowledge several difficulties. These came up in several
application areas. I’ll mention just a few.
In attempting to prove nontrivial mathematical theorems, for example,
theorem-proving programs quickly exhausted the space necessary to store
intermediate results. But humans (well, some humans at least) are able to
prove theorems. What methods are they using that computer programs are
not? Mathematicians would probably say that intuition, judgment, experience,
mathematical sophistication, and such are critical to their successes. So far, it
has proven difficult to provide computers with these capabilities.
In game playing, although MAC HACK VI and CHESS 4.6, to name two
examples, played pretty good chess, they were far from being able to beat
world champions. In fact, in August 1978 at the Canadian National Exhibition
in Toronto, David Levy defeated the reigning Computer Chess Champion,
CHESS 4.7, from Northwestern University, thus winning the bet he made ten
years before against John McCarthy and Donald Michie. As Levy put it, “I
managed to beat the program fairly convincingly, by three wins to one with
one game drawn (the sixth game did not need to be played) and with this
match I won my bet.”44
What accounts for championship ability? It’s unlikely that chess
champions look at more chess positions than computers do. However, they
406
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![24.4 NOTES
days, there’s a mood that I’m sure many of you are familiar with of
deep unease among AI researchers who have been around more
than the last four years or so. This unease is due to the worry that
perhaps expectations about AI are too high, and that this will
eventually result in disaster.
. . . I think it is important that we take steps to make sure the “AI
Winter” doesn’t happen – by disciplining ourselves and educating
the public.
But if “disciplining” and “educating” did take place, they were insufficient
to prevent the worried-about downturn. During the late 1980s, membership in
the AAAI gradually fell. By 1996, it had leveled off to between 4,000 and
5,000 members. Advertising in the AI Magazine dropped also – as did
participation by government and industry in AI conference exhibits. Several
AI companies closed their doors, and AI research at some of the larger
computer hardware and software companies was terminated. According to
Alex Roland, between 1987 and 1989, DARPA’s budget for basic AI and
Strategic Computing research fell from $47 million to $31 million. (Even so,
according to Alex Roland, CMU’s budget was increased for its speech
understanding program and its autonomous vehicle program during this time.)
But the winter endured only for a season – a season not of hibernation but
of renewed efforts to carry on. Several new ideas were explored, and older ones
were strengthened with added powers, as I’ll explain in subsequent chapters.
Notes
1. Hilary Putnam, “Much Ado about Not Very Much,” Dædalus (Special Issue on Artificial
Intelligence), pp. 269–281, Winter 1988. [381]
2. Alan Ross Anderson (ed.), Minds and Machines, Englewood Cliffs, NJ: Prentice-Hall,
Inc., 1964. [381]
3. John R. Lucas, “Minds, Machines, and Gödel,” Philosophy, Vol. XXXVI, pp. 112–127,
1961; reprinted in Alan Ross Anderson (ed.), Minds and Machines, pp. 43–59, Englewood
Cliffs, NJ: Prentice-Hall, Inc., 1964; available online at
http://users.ox.ac.uk/∼jrlucas/Godel/mmg.html. [382]
4. Lucas himself lists some references to these in a Web site,
http://users.ox.ac.uk/∼jrlucas/Godel/referenc.html. [382]
5. John R. Lucas, The Freedom of the Will, Oxford: Oxford University Press, 1970. Lucas
claims on his Web site that the book is out of print but is now available from Oxford
University Press “on a one-off basis.” [382]
6. Available online at http://users.ox.ac.uk/∼jrlucas/Godel/brighton.html. [382]
7. Mortimer Taube, Computers and Commonsense: The Myth of Thinking Machines, New
York: Columbia University Press, 1961. [383]
8. Roger Penrose, The Emperor’s New Mind: Concerning Computers, Minds and the Laws
of Physics, New York: Random House, Inc., 1989. [383]
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![24 NOTES
9. Roger Penrose, Shadows of the Mind: A Search for the Missing Science of
Consciousness, Oxford: Oxford University Press, 1994. [383]
10. Roger Penrose, “Beyond the Doubting of a Shadow: A Reply to Commentaries on
Shadows of the Mind,” PSYCHE, Vol. 2, p. 23, January 1996. Available online (with
pointers to articles by critics) at
http://psyche.cs.monash.edu.au/v2/psyche-2-23-penrose.html. [383]
11. See John R. Searle, “Minds, Brains, and Programs,” Behavioral and Brain Sciences,
Vol. 3, No. 3, pp. 417–457, 1980. Available online at
http://www.bbsonline.org/documents/a/00/00/04/84/bbs00000484-00/bbs.searle2.html.
[383]
12. Ibid. [384]
13. Allen Newell and Herbert A. Simon, “Computer Science as Empirical Inquiry: Symbols
and Search,” Communications of the ACM. Vol. 19, No. 3, pp. 113–126, March 1976.
Available online at
http://www.rci.rutgers.edu/∼cfs/472 html/AI SEARCH/PSS/PSSH1.html. [387]
14. For an online interview with Dreyfus about his career, see
http://globetrotter.berkeley.edu/people5/Dreyfus/dreyfus-con0.html. [389]
15. Hubert L. Dreyfus, “Alchemy and Artificial Intelligence,” RAND paper P-3244, The
RAND Corporation, Santa Monica, CA, December 1965. Available online at
http://www.rand.org/pubs/papers/2006/P3244.pdf. [389]
16. John Haugeland, Artificial Intelligence: The Very Idea, Cambridge, MA: MIT Press,
1985. [391]
17. See, for example, Hubert L. Dreyfus, What Computers Can’t Do: A Critique of
Artificial Reason, New York: Harper & Row, 1972 (second edition, 1979). A revised edition
with the title What Computers Still Can’t Do: A Critique of Artificial Reason was
published by MIT Press, 1992. Hubert L. Dreyfus and Stuart E. Dreyfus, Mind Over
Machine: The Power of Human Intuition and Expertise in the Era of the Computer, New
York: Free Press, 1986. [391]
18. Pamela McCorduck, Machines Who Think: A Personal Inquiry into the History and
Prospects of Artificial Intelligence, San Francisco: W. H. Freeman and Co., 1979. [391]
19. Hubert L. Dreyfus, “Overcoming the Myth of the Mental: How Philosophers Can Profit
from the Phenomenology of Everyday Expertise,” Presidential Address, Proceedings and
Addresses of the American Philosophical Association, Vol. 79, No. 2, November 2005.
Available online at http://socrates.berkeley.edu/∼hdreyfus/pdf/Dreyfus%20APA%
20Address%20%2010.22.05%20.pdf. [391]
20. Hubert L. Dreyfus, What Computers Still Can’t Do: A Critique of Artificial Reason, p.
255, Cambridge, MA: MIT Press, 1992. [392]
21. E-mail correspondence of August 9, 2007. For Dreyfus’s comments about Heidegger, see
Hubert L. Dreyfus, Being-in-the-World: A Commentary on Heidegger’s Being and Time,
Division I, Cambridge, MA: MIT Press, 1991. [392]
22. Hubert L. Dreyfus, “Why Heideggerian AI Failed and How Fixing It Would Require
Making It More Heideggerian,” (a paper written in connection with being awarded the
APA’s Barwise Prize, 2006), Philosophical Psychology, Vol. 20, No. 2, pp. 247–248, 2007;
reprinted in Michael Wheeler (ed.), The Mechanization of Mind, Cambridge MA: MIT
Press, in press. [392]
23. Rolf Pfeifer, Max Lungarella, and Fumiya Iida, “Self-Organization, Embodiment, and
Biologically Inspired Robotics,” Science, Vol. 318, No. 5853, pp. 1088–1093, November 16,
2007. [392]
24. For example, a paper written in 2003 claimed that “Google’s architecture features
410
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![24.4 NOTES
clusters of more than 15,000 commodity-class PCs with fault-tolerant software.”
Undoubtedly, Google uses many more networked computers today. See Luiz André Barroso,
Jeffrey Dean, and Urs Hölzle, “Web Search for a Planet: The Google Cluster Architecture,”
IEEE Micro, March–April 2003. Available online at
http://labs.google.com/papers/googlecluster-ieee.pdf. [393]
25. Joseph Weizenbaum, Computer Power and Human Reason: From Judgment to
Calculation, San Francisco: W. H. Freeman and Co., 1976. [394]
26. Ibid, pp. 223–224. [394]
27. Ibid, pp. 202–203. [394]
28. Ibid, p. 227. [394]
29. Lewis Thomas, “Notes of a Biology Watcher: On Artificial Intelligence,” The New
England Journal of Medicine, Vol. 302, No. 9, pp. 506ff, February 28, 1980. [396]
30. Theodore Roszak, The Cult of Information: A Neo-Luddite Treatise on High-Tech,
Artificial Intelligence, and the True Art of Thinking, second edition, Berkeley, CA:
University of California Press, 1994. [396]
31. Ibid, pp. xviii–xix. [396]
32. Ibid, p. xiv. [396]
33. Ibid, p. 232. [397]
34. Ibid, p. 232. [397]
35. Ibid, p. xxiv. [397]
36. Ibid, p. 232. [397]
37. All responses are in my files. [398]
38. The online encyclopedia Wikipedia mentions the slogan in its entry for Pierce at
http://en.wikipedia.org/wiki/John R. Pierce. [398]
39. For a proof about sliding tile puzzles, see Daniel Ratner and Manfred Warmuth,
“Finding a Shortest Solution for the N*N-extension of the 15-puzzle Is Intractable,” Journal
of Symbolic Computing, Vol. 10, pp. 111–137, 1990. [402]
40. See http://www.claymath.org/prizeproblems/index.htm. [402]
41. Jacob Schwartz, “Limits of Artificial Intelligence,” in Stuart C. Shapiro and David
Eckroth (eds.), Encyclopedia of Artificial Intelligence, Vol. 1, pp. 488–503, New York: John
Wiley and Sons, Inc. 1987. [403]
42. W. W. Johnson and W. E. Story, “Notes on the 15 Puzzle,” American Journal of
Mathematics, Vol. 2, pp. 397–404, 1879. I thank Richard Korf for this citation. [405]
43. Richard E. Korf and L. A. Taylor, “Finding Optimal Solutions to the Twenty-Four
Puzzle,” Proceedings of the Thirteenth National Conference on Artificial Intelligence and
the Eighth Innovative Applications of Artificial Intelligence Conference, pp. 1202–1207,
Menlo Park, CA: AAAI Press and Cambridge, MA: MIT Press, August 1996. [405]
44. David Levy, Robots Unlimited: Life in a Virtual Age, p. 84, Wellesley, MA: A. K.
Peters, Ltd., 2006. [406]
45. See the paper by John McCarthy, “Some Expert Systems Need Common Sense,” Heinz
Pagels (ed.), Computer Culture: The Scientific, Intellectual and Social Impact of the
Computer, Annals of the New York Academy of Sciences, Vol. 426, November 1995.
Available online at http://www-formal.stanford.edu/jmc/someneed/someneed.html. [407]
46. Benjamin Kuipers, “On Representing Commonsense Knowledge, in Nicholas V. Findler
(ed.), Associative Networks: The Representation and Use of Knowledge by Computers, pp.
393–408, New York: Academic Press, 1979. Available online at
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![24 NOTES
ftp://ftp.cs.utexas.edu/pub/qsim/papers/Kuipers-csk-79.ps.gz. [407]
47. See Drew McDermott, M. Mitchell Waldrop, B. Chandrasekaran, John McDermott, and
Roger Schank, “The Dark Ages of AI: A Panel Discussion at AAAI-84,” AI Magazine, Vol.
6, No. 3, pp. 122–134, Fall 1985. [408]
412
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![25.4 About Behavior
neuroscience, you’ll see that a human brain has many hundreds of
parts – that is, subcomputers – that do different things. Why do
our brains need so many parts? Surely, if our minds were based on
only a few basic principles, we wouldn’t need so much complexity.
Of course, just because the brain is a kludge does not mean that
computer intelligences have to be. Nevertheless, some AI researchers favored
systems consisting of collections of experimentally derived, ad hoc routines
designed to solve specific problems. These people called themselves “scruffies”
to distinguish themselves from the “neats” who favored programs based on
theoretically based principles. (These terms were apparently first used by
Roger Schank in the 1970s to contrast his approach to building natural
language processing systems with the more theoretically based work of
McCarthy and others.) In his keynote address at the 1981 annual meeting of
the Cognitive Science Society, Robert Abelson compared the two camps by
saying “The primary concern of the neat is that things should be orderly and
predictable while the scruffy seeks the rough-and-tumble of life as it
comes. . . ”10
I believe that both neats and scruffies are needed in a field as immature as
AI is. Scruffies are better at exploring frontiers outside the boundaries of
well-established theory. Neats help codify newly gained knowledge so that it
can be taught, written about, and thus remembered.
25.4 About Behavior
25.4.1 Behavior-Based Robots
Using an approach that harkens back to Grey Walter’s “tortoises,” the MIT
computer scientist Rodney Brooks eschewed complex representations and
reasoning processes and focused instead on what he called a “behavior-based
approach to building robots that operate in the real world.”
Brooks wrote that his approach drew inspiration from attempting to
[r]ecapitulate evolution, or an approximation thereof, as a design
methodology, in that improvements in performance come about by
incrementally adding more situation specific circuitry [or software
organized like circuitry] while leaving the old circuitry in place, able
to operate when the new circuitry fails to operate (most probably
because the perceptual conditions do not match its preconditions
for operating). Each additional collection of circuitry is referred to
as a new layer. Each new layer produces some observable new
behavior in the system interacting with its environment.11
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![25.4 About Behavior
one on top of another, incrementally. Each layer handled increasingly complex
modes of walking and “subsumed” (overrided when appropriate) the layer
below when the layer below was not able to handle the current situation.
Brooks called this type of layered organization a “subsumption architecture.”
The circuitry consisted of simple computational devices called “augmented
finite-state machines” (implemented by 8-bit microprocessors).12
Unlike earlier symbolic approaches, Brooks’s approach to robotics did not
use central models of the environment and programs to “plan” courses of
action. He argued that “the symbol system hypothesis upon which classical AI
is based is fundamentally flawed. . . ”13
Instead, Brooks wrote
. . . the specific goals of the robot are never explicitly represented
[in the behavior-based approach], nor are there any plans – the
goals are implicit in the coupling of actions to perceptual
conditions, and apparent execution of plans unroll in real-time as
one behavior alters the robot’s configuration in the world in such a
way that new perceptual conditions trigger the next step in a
sequence of actions.14
In his paper “Elephants Don’t Play Chess,”15
Brooks gives examples of
several other quite interesting robot systems developed in his MIT lab. The
title of Brooks’s paper is meant to indicate that quite complex behavior (such
as the behavior of elephants for example) can be achieved with systems that
(presumably) do not have the representational and reasoning powers required
for intelligent activities such as playing chess. Yet, although AI scientists
would certainly be pleased to be able to build machines with the intelligence of
elephants, achieving AI’s ultimate goals would seem to require complex
representational and reasoning methods beyond what the behavior-based
approaches are able to offer.
Although I think that following along the path (or paths) of the evolution
of ever-more capable and intelligent animals has a lot to recommend it, I don’t
think we are very far along in going “from earwigs to humans” (to use the title
of one of Brooks’s articles) – let alone in getting up to elephants.16
25.4.2 Teleo-Reactive Programs
Even though it’s doubtless not the whole story, coordinating behavior with
ongoing perceptual input is an important part of an intelligent system. It’s a
part that I have been interested in ever since the days of working on
intermediate-level actions for Shakey the robot. I was able to return to
thinking about behavioral control during a sabbatical year in 1990 and 1991. I
spent part of that year in Brooks’s laboratory at MIT. There, aided by some
important suggestions made by a Stanford (and soon-to-be MIT) student,
Mark Torrance, I developed what I called the “teleo-reactive” (T-R)
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![25 Controversies and Alternative Paradigms
so the robot activates the associated primitive action move-forward. In the
meantime and while the move-forward action is proceeding (this is
important!), the part of the robot system that is checking to see which is the
first true line in the program is still checking (in the background, as it were).
Sooner or later (if we assume the robot does not change its “heading” while it
is moving forward), the condition part of line 1 will become true. Precisely at
that time, line 3 is no longer the first line of the program whose condition part
is true; line 1 is. So, line 3’s action part is suspended, line 1’s action part is
activated, and the ball is kicked away.
Now, to illustrate the robustness of T-R programs and to explain how
subprograms are activated, let’s assume everything is the same as before
(namely, the robot is facing the ball and is far away from the ball) but that,
during the time that the robot is moving forward (because move-forward is
being activated), the robot inadvertently drifts off course so that it is no
longer facing the ball. At the time the robot perceives this change, line 3 of
the program is no longer the first line whose condition part is true – line 4 is
(because its condition, namely, True is assumed always to be true.) So at that
time activation of move-forward ceases, and instead moveto(Ball) is
activated.
To activate moveto(Ball), the program moveto(x) is retrieved from the
“program library,” and its parameter, x, is replaced by Ball, and the following
program is activated:
moveto(Ball):
1. Close(Ball) -> do-nothing
2. Facing(Ball) -> move-forward
3. True -> face(Ball)
The first line of this program whose condition part is true is line 3 – resulting
in activating face(Ball), another subprogram. If we assume that the robot’s
drift off its heading resulted in the ball being to its left, activation of
face(Ball) will cause the robot to rotate in a counterclockwise direction.
Sooner or later, the robot will be facing the ball again. Now, an interesting
thing happens. The subprogram moveto(Ball), with all of its
condition-checking apparatus, is still running in the background. Its line 2 is
now the first line in the program whose condition part is true (instead of its
line 3 as before). So, the face(Ball) program ceases operation and the
move-forward primitive program is activated. If nothing further untoward
happens, line 1 of kick(x) will be the first line in that program whose
condition part is true [moveto(Ball) will be suspended], and foot-swing will
be activated. (Whew! It’s easier for the circuitry that controls all of this to
function automatically than it is for us to think about it.) If you aren’t
exhausted, you might want to consider some of the other ways that these
programs might be activated.
422
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![25.7 Scaling Back AI’s Goals
computation including GAs and GPs.28
A compelling narrated video
demonstration of the power of simulated evolution, presented at the 1991
SIGGRAPH conference, can be viewed at
http://www.archive.org/details/sims evolved virtual creatures 1994. As
described by the Web site, “A population of several hundred creatures is
created within a supercomputer, and each creature is tested for [its] ability to
perform a given task, such [as] the ability to swim in a simulated water
environment.”29
25.7 Scaling Back AI’s Goals
During the AI winter, many AI researchers began to focus on more modest
and achievable goals than on those of previous years. One heard fewer brave
predictions about what AI could ultimately achieve. Increasingly, effort was
devoted to what AI could (at the time) actually achieve. The result was more
work on limited or “weak” AI and less on “strong AI.” The emphasis was on
using AI to help humans rather than to replace them.
Companies and government agencies with funds to support research
looked to computer technologies generally (rather than to AI specifically) to
help solve their problems. Research funds were directed at improving database
systems, user interfaces, graphics, computer networks, data mining, computer
games, information retrieval, computer vision, and word-processing and
spreadsheet programs, to name just a few areas. The AI technologies of search
and inference, expert systems, speech recognition, and natural language
processing were used, when appropriate, as components of large, integrated
systems. AI researchers began to be satisfied with adding bits of intelligence
here and there to these systems to make them more useful and appealing.
Notes
1. Michael Genesereth and Nils Nilsson, Logical Foundations of Artificial Intelligence, San
Francisco: Morgan Kaufmann Publishers, 1987. [413]
2. Drew McDermott, “A Critique of Pure Reason,” Computational Intelligence, Vol. 3, No.
3, pp. 151–160, August 1987. [413]
3. For a brief online summary with citations to longer articles, see Glenn Shafer,
“Dempster–Shafer Theory,” 2002; available at
http://www.glennshafer.com/assets/downloads/articles/article48.pdf. [414]
4. See, for example, David L. Hall and Sonya A. H. McMullen, Mathematical Techniques in
Multisensor Data Fusion, Norwood, MA: Artech House, Inc., 2004. [414]
5. Zadeh’s original article is Lotfi Zadeh, “Fuzzy Sets,” Information and Control, Vol. 8,
pp. 338–353, 1965; available online at http://www-bisc.cs.berkeley.edu/Zadeh-1965.pdf. For
a summary, see E. Cox, “Fuzzy Fundamentals,” IEEE Spectrum, Vol. 29, No. 10, pp. 58–61,
1992. The Association for the Advancement of Artificial Intelligence (AAAI) maintains a
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![25 NOTES
fuzzy logic Web page (with lots of pointers to tutorials, papers, and applications) at
http://www.aaai.org/AITopics/html/fuzzy.html. [414]
6. See, for example, Kevin M. Passino and Stephen Yurkovich, Fuzzy Control, Menlo Park,
CA: Addison Wesley Longman, 1998. (The book is no longer in print but can be
downloaded from http://www.ece.osu.edu/∼passino/FCbook.pdf.) [415]
7. See Machine Design, March 1995. [415]
8. See The Berkeley Initiative in Soft Computing Web site at
http://www-bisc.cs.berkeley.edu/. [416]
9. Marvin Minsky, “Smart Machines,” Chapter 8 of John Brockman, The Third Culture:
Beyond The Scientific Revolution, New York: Simon & Schuster, 1995. Available online at
http://edge.org/documents/ThirdCulture/p-Ch.8.html. The whole book is available at
http://www.edge.org/documents/ThirdCulture/d-Contents.html. [416]
10. The quotation is taken from Wendy G. Lehnert, “Cognition, Computers, and Car
Bombs: How Yale Prepared Me for the 1990s,” in Roger Schank and Ellen Langer (eds.),
Beliefs, Reasoning, and Decision Making: Psycho-Logic in Honor of Bob Abelson, pp.
143–173, Hillsdale, NJ: Lawrence Erlbaum Associates, 1994. [417]
11. Rodney A. Brooks, “From Earwigs to Humans,” Proceedings IIAS The Third Brain and
Mind International Symposium on Concept Formation, Thinking and Their Development,
pp. 59–66, Kyoto, Japan, May 1996. Available online at
http://people.csail.mit.edu/brooks/papers/ascona.pdf. [417]
12. For a description of how Genghis works, see Rodney A. Brooks, “A Robot That Walks:
Emergent Behavior from a Carefully Evolved Network,” Neural Computation, Vol. 1, No. 2,
pp. 253–262, Summer 1989. Also in Proceedings of the IEEE International Conference on
Robotics and Automation, pp. 292–296, Scottsdale, AZ, May 1989. Available online as an
MIT AI Lab Memo (No. 1091) at http://people.csail.mit.edu/brooks/papers/AIM-1091.pdf.
[419]
13. Rodney A. Brooks, “Elephants Don’t Play Chess,” Robotics and Autonomous Systems,
Vol. 6, pp. 3–15, 1990. Also in Pattie Maes (ed), Designing Autonomous Agents: Theory
and Practice from Biology to Engineering and Back, pp. 3–15, Cambridge, MA: MIT Press,
1990. Available online at http://people.csail.mit.edu/brooks/papers/elephants.pdf. [419]
14. Rodney A. Brooks, op. cit. [419]
15. Rodney A. Brooks, op. cit. [419]
16. See Brooks’s Web pages for pointers to others of his publications:
http://people.csail.mit.edu/brooks/. [419]
17. David E. Rumelhart, “Brain Style Computation: Learning and Generalization,” in
Steven E Zornetzer, Joel L. Davis, and Clifford Lau (eds.), An Introduction to Neural and
Electronic Networks, San Diego: Academic Press, 1990. [424]
18. James L. McClelland, David E. Rumelhart, and the PDP Research Group, Parallel
Distributed Processing, Explorations in the Microstructure of Cognition, Volume 1:
Foundations, Cambridge, MA: MIT Press, 1986, and James L. McClelland, David E.
Rumelhart, and the PDP Research Group, Parallel Distributed Processing, Explorations in
the Microstructure of Cognition, Vol. 2: Psychological and Biological Models, Cambridge,
MA: MIT Press, 1986. [424]
19. John J. Hopfield, “Neural Networks and Physical Systems with Emergent Collective
Computational Abilities,” Proceedings of the National Academy of Science, Vol. 79, No. 8,
pp. 2554–2558, 1982. Available online from
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=346238. [424]
20. See, for example, Jerome A. Feldman et al., “Computing with Structured Connectionist
Networks,” Communications of the ACM, Vol. 31, No. 2, pp. 170–187, February 1988. [424]
430
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![25.7 NOTES
21. T. J. van Gelder, “Dynamic Approaches to Cognition” in R. Wilson and F. Keil (eds.),
The MIT Encyclopedia of Cognitive Sciences, pp. 244–246, Cambridge MA: MIT Press,
1999. Available online at http://sites.google.com/site/timvangelder/publications-1/
dynamic-approaches-to-cognition/MITDyn.pdf?attredirects=0. [424]
22. Randall D. Beer,“Dynamical Approaches to Cognitive Science,” Trends in Cognitive
Sciences, Vol. 4, No. 3, March 2000; available online at
http://mypage.iu.edu/∼rdbeer/Papers/TICS.pdf. For a longer paper, see Randall D. Beer,
“A Dynamical Systems Perspective on Agent-Environment Interaction,” Artificial
Intelligence, Special Volume on Computational Research on Interaction and Agency, Part 1,
Vol. 72, Nos. 1–2, pp. 173–215, 1995; available online at
http://mypage.iu.edu/∼rdbeer/Papers/AIJ95.pdf. Beer’s Web pages
(http://mypage.iu.edu/∼rdbeer/) provide many additional citations. [425]
23. For more about these kinds of simple robots, visit the Wikipedia site
http://en.wikipedia.org/wiki/BEAM robotics. [425]
24. See also Melanie Mitchell, An Introduction to Genetic Algorithms, Cambridge, MA:
MIT Press, 1996. [427]
25. The quotation is from Koza’s homepage at
http://www.genetic-programming.com/johnkoza.html. [428]
26. See Forest H. Bennett et al., “Building a Parallel Computer System for $18,000 That
Performs a Half Peta-flop per Day,” in Wolfgang Banzhaf et al. (eds.), GECCO-99:
Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1484–1490, San
Francisco, CA: Morgan Kaufmann Publishers, 1999. [428]
27. See, for example, John R. Koza, Genetic Programming: On the Programming of
Computers by Means of Natural Selection, Cambridge, MA: MIT Press, 1992; John R. Koza,
Genetic Programming II: Automatic Discovery of Reusable Programs, Cambridge, MA: MIT
Press, 1994; John R. Koza, Forrest H Bennett III, David Andre, and Martin A. Keane,
Genetic Programming III: Darwinian Invention and Problem Solving, San Francisco:
Morgan Kaufmann Publishers, 1999; and John R. Koza, Martin A. Keane, Matthew J.
Streeter, William Mydlowec, Jessen Yu, and Guido Lanza, Genetic Programming IV:
Routine Human-Competitive Machine Intelligence, Norwell, MA: Kluwer Academic
Publishers, 2003. [428]
28. Visit the SIGEVO Web site at http://www.sigevo.org/index.html. [429]
29. I thank Mykel Kochenderfer for telling me about this video. [429]
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![26 Reasoning and Representation
Soon after, another MIT student, Johan de Kleer (1951– ), wrote a
program called NEWTON for his master’s thesis that included a component
able to do qualitative reasoning. NEWTON, de Kleer claimed, “understands
and solves problems in a mechanics mini-world of objects moving on
surfaces.”9
NEWTON used its qualitative knowledge about physics to produce
approximate problem solutions, which it then used to plan and carry out
subsequent quantitative calculations. Most physics teachers will tell you that
qualitative reasoning about “the physics” of a problem is essential before
plunging into the mathematics.
In doing qualitative reasoning, NEWTON used a process called
“envisioning” for “generating a progression of scenes encoded in a symbolic
description which describe what could happen.” It used descriptions of six
basic actions appropriate to the kinds of problems NEWTON could solve. One
of these descriptions, for example, was for FLY. It encoded the knowledge that
“[i]f the object is moving on top of a surface which is concave away from the
motion, the object might fly off.” de Kleer’s main contribution was to show
how qualitative calculations and quantitative reasoning can be combined in a
computer program.
In 1979 Pat Hayes published “The Naive Physics Manifesto.”10
A revised
version appeared in 1985.11
He proposed that the artificial intelligence
research community begin “the construction of a formalization of a sizable
portion of common-sense knowledge about the everyday physical world: about
objects, shape, space, movement, substances (solids and liquids), time, etc.”
These topics had long presented particularly difficult representational and
reasoning challenges for AI. Encoding our everyday knowledge about these
subjects so that computers can reason about them is at the heart of
qualitative physics, which Hayes called “naive physics.”
His manifestos presented some general ideas about how to represent
“clusters” of common-sense knowledge about the physical world. As one
example, he proposed the notion of “histories” for representing events, instead
of states and functions of states as he and John McCarthy had earlier
advocated. He defined a history as “a piece of spacetime with natural
boundaries, both temporal and spatial.” For example, “the event of putting
four blocks together in a square is the beginning of the history of a platform,
and the end of that history is when and where they are separated from one
another.”
Hayes said, in effect, that we should not be “too hasty” about writing
naive physics programs – preferring instead to delay implementations until
more foundational work had been done on the representations themselves. He
had already sketched out some of this work on liquids.12
These initial explorations in qualitative reasoning soon led to a rapidly
growing subfield of AI with many applications, especially in diagnosing faults
based on qualitative models of equipment. (I’ll mention one example,
440
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![26.3 Semantic Networks
relationship between semantic networks and logic.18
) Expanding on the ideas
in his thesis, Brachman, along with other colleagues at BBN (including
Woods) and at USC-ISI, developed a frame-based knowledge representation
system called KL-ONE,19
which became the basis for what came to be called
description logics.
Levesque did all of his college work (B.S., M.S., and Ph.D.) at the
University of Toronto. After receiving his Ph.D. degree in 1981, he joined
Brachman at the Fairchild Laboratory for Artificial Intelligence Research in
Palo Alto, a group founded by Peter Hart after Hart left SRI. There,
Brachman and Levesque, together with Richard Fikes (then at Xerox PARC),
developed the KRYPTON representation and reasoning system.20
KRYPTON
was a hybrid system – meaning it represented knowledge both by logical
formulas and by a semantic network.
Although semantic networks make it easy to reason about individuals and
their properties in a hierarchy, it is difficult for them to represent statements
containing negations and disjunctions. As the KRYPTON paper states, “. . . a
statement such as ‘either Elsie or Bessie is the cow standing in Farmer Jones’s
field’ cannot be made in a typical assertional frame system.” KRYPTON’s
solution is to use a combination of both types of representations:
. . . we have split the [reasoning] operations into two separate kinds,
yielding two main components for our representation system: a
terminological [that is, network-based] one, or T Box, and an
assertional [that is, logic-based] one, or A Box. The T Box allows
us to establish taxonomies of structured terms and answer
questions about analytical relationships among these terms; the A
Box allows us to build descriptive theories of domains of interest
and to answer questions about those domains.21
The diagram from the KRYPTON paper shown in Fig. 26.4 illustrates the
structure of the system. As the authors wrote, it consists of “a T Box of
structured terms organized taxonomically, an A Box of (roughly) first-order
sentences whose predicates come from the T Box, and a symbol table
maintaining the names of the T Box terms so that a user can refer to them.”
The T Box in the diagram represents (among other things) that a child is a
person. The A Box states that there exists a doctor who has a child.
KRYPTON was the forerunner of several description-logic systems,
including CLASSIC, developed by Brachman and colleagues after he moved to
ATT Bell Laboratories.22
In addition to their use in AI reasoning systems,
description logics are used in ontology languages for the semantic Web, for
example DAML-ONT23
and OWL.24
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![26 Reasoning and Representation
Figure 26.4: Parts of a KRYPTON T Box and A Box. (Adapted from Ronald J.
Brachman, Richard E. Fikes, and Hector J. Levesque, “KRYPTON: A Functional
Approach to Knowledge Representation, IEEE Computer, Vol. 16, No. 10, p.
71, October 1983.)
26.3.2 WordNet
WordNet is a large “conceptual” dictionary of English words, organized
somewhat like a semantic network and inspired by psycholinguistic and
computational theories of human lexical memory.25
Its development was
begun at Princeton University in the 1980s under the direction of Professor
George A. Miller (the same George Miller whom I mentioned earlier and who
wrote “The Magical Number Seven, Plus or Minus Two”). In a 1990 paper,
Miller and his colleagues had this to say about the beginnings of the project:26
In 1985 a group of psychologists and linguists at Princeton
University undertook to develop a lexical database along lines
suggested by [earlier psycholinguistic] investigations. The initial
idea was to provide an aid to use in searching dictionaries
conceptually, rather than merely alphabetically – it was to be used
in close conjunction with an on-line dictionary of the conventional
444
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![26.3 Semantic Networks
heuristics,31
decided to undertake the immense task of providing computers
with common-sense knowledge. (See Fig. 26.5.) The first step, he thought,
would be to “prime the pump with the millions of everyday terms, concepts,
facts, and rules of thumb” that comprise common sense. As he later described
it, the project began this way:32
In the fall of 1984, Admiral Bobby Ray Inman convinced me that if
I was serious about taking that first step, I needed to leave
academia and come to his newly formed MCC (Microelectronics
and Computer Consortium) in Austin, Texas, and assemble a team
to do it. The idea was that over the next decade dozens of
individuals would create a program, Cyc, with common sense. We
would “prime the knowledge pump” by handcrafting and
spoon-feeding Cyc with a couple of million important facts and
rules of thumb.
The name “Cyc” (pronounced like “psych”) comes from three letters in
the middle of the word “encyclopedia.” The idea was that if Cyc had enough
knowledge to understand articles in an encyclopedia, it would be able to read
all kinds of material and acquire additional knowledge on its own – a “second
step” toward smart computers. (The “third step” would be to conceive of and
perform experiments to gain more knowledge, that is, knowledge beyond what
humans already know.)
To understand encyclopedia articles, humans must already know quite a
bit about the world. As Lenat put it:33
If we take any sentence from an encyclopedia article and think
about what the writer assumes the reader already knows about the
world, we will have something worth telling Cyc. Alternatively, we
can take a paragraph and look at the “leaps” from one sentence to
the next and think about what the writer assumes the reader will
infer “between” the sentences. [Consider, for example, the
sentences] “Napoleon died on St. Helena. Wellington was greatly
saddened.” The author expects the reader to infer that Wellington
heard about Napoleon’s death, that Wellington outlived Napoleon,
and so on.
Just how much knowledge would Cyc need to have to understand articles
in an encyclopedia? Lenat recently told me that he originally thought Cyc
would need “a couple million general assertions, such as ‘mammals have hair’
(plus a vastly larger number of specific facts, such as what the capital of
California is).” Now he believes that “the number is more like 200 million.”34
Lenat and his team of programmers and “knowledge enterers” worked
away on Cyc, entering knowledge by hand, for about ten years at MCC.
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![26 NOTES
Cyc to get information from patient databases.38
The researcher types in his or
her query in English. “Cyc parses what it can, recognizing some portions of the
query, and presents the user with a set of partial query ‘fragments’ that are
like fill-in-the-blank templates.” Cyc then uses its specialized knowledge about
medicine plus its common-sense knowledge to figure out how to paraphrase the
query for the user to check. On getting user agreement about what the user
asked, it uses its knowledge about how the database is organized to generate
database queries to retrieve the desired information.
There are two versions of Cyc available for download. One is called
ResearchCyc and is available to the research community (for research-only
purposes) under a ResearchCyc license.39
Besides the Cyc Inference Engine, it
contains “nearly 3,000,000 assertions (facts and rules), using 26,000+
relations, that interrelate, constrain, and, in effect, (partially) define the
concepts.” Another, called OpenCyc, is a publicly available open source version
of the Cyc technology.40
It contains “hundreds of thousands of terms, along
with millions of assertions relating the terms to each other. . . .” One can also
examine the concept hierarchy in OpenCyc using an online browser.41
No one knows exactly how humans organize and use their common-sense
(and expert) knowledge. Whether the facts and relations already amassed
(and yet to be gathered) by the Cyc project will be adequate in amount and
organization to permit human-level reasoning has yet to be demonstrated.
Yet, I applaud the effort and wish the project well. Certainly, I think
something at least as ambitious as Cyc will be required. (Another attempt to
gather common-sense knowledge is that of the “Commonsense Computing
Initiative” at the MIT Media Lab. The work there is described at
http://xnet.media.mit.edu/.)
It’s possible that Cyc might get to the point where (with some human
help) it will be able to gather more of the required knowledge directly from the
Internet. Lenat mentions42
a game called “FACTory,” designed to help gather
knowledge from humans who play the game. You can play it at
http://game.cyc.com/game.html. In the game, Cyc generates natural language
statements it has gathered from English sentences it has found on the Web. It
presents these statements to ten randomly chosen players of the game. If
enough of them answer that the statement is “true,” Cyc adds that fact to its
KB (and the players get points in the game). I tried the game, and Cyc asked
me whether or not “All spaghetti marinara includes some garlic.” I answered
“true,” and Cyc said that I agreed with 66% of the other players and that it
now (therefore) believes the sentence is “true.”
Notes
1. Margery Cuyler, That’s Good! That’s Bad!, New York: Henry Holt and Co., 1991. [435]
450
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![26.3 NOTES
2. Raymond Reiter, “A Logic for Default Reasoning,” Artificial Intelligence, Vol. 13, pp.
81–132, 1980. [437]
3. John McCarthy, “Circumscription – A Form of Non-monotonic Reasoning,” Artificial
Intelligence, Vol. 13, pp. 27–39, 1980. There are several papers about circumscription by
Vladimir Lifschitz, for example, “On the Satisfiability of Circumscription,” Artificial
Intelligence, Volume 28, No. 1, pp. 17–27, 1986. [438]
4. See, for example, John McCarthy, “Applications of Circumscription to Formalizing
Common Sense Knowledge,” Artificial Intelligence, Vol. 28, No. 1, pp. 89–116, 1986. [438]
5. For people who are interested in the history of the problem, the major players, and the
technical details of its alleged solution, see M. Kamermans and Tijn Schmits, “The History
of the Frame Problem,” available online from
http://student.science.uva.nl/∼tschmits/Bachelorproject/index.html, 2004. (The paper has
a nice chart summarizing this history, which is available separately at
http://student.science.uva.nl/∼tschmits/Bachelorproject/poster HotFP.PNG. See also
Murray Shanahan, Solving the Frame Problem: A Mathematical Investigation of the
Common Sense Law of Inertia, Cambridge, MA: MIT Press, 1997. [438]
6. See, for example, papers in collections by Matt Ginsberg (ed.), Readings in
Nonmonotonic Reasoning, Los Altos, CA: Morgan Kauffman Publishers, 1987, and D.
Gabbay, C. Hogger, and J. Robinson (eds.), Handbook of Logic in Artificial Intelligence and
Logic Programming, Vol. 3, Oxford and New York: Oxford University Press, 1994. [438]
7. I found the wave equation on a Web site belonging to a manufacturer of surfing
products: http://www.waveequation.com/wave equation data.html. [439]
8. Scott E. Fahlman, “A Planning System for Robot Construction Tasks,” Artificial
Intelligence, Vol. 5, No. 1, pp. 1–49, 1974. The thesis is available online as an MIT AI
Laboratory Technical Report No. 283 with the same title and dated May 1973 at
http://dspace.mit.edu/bitstream/handle/1721.1/6918/AITR-283.pdf?sequence=2. [439]
9. Johan de Kleer, “Qualitative and Quantitative Knowledge in Classical Mechanics,”
Artificial Intelligence Laboratory, Technical Report 352, December 1975. Available online at
http://dspace.mit.edu/bitstream/handle/1721.1/6912/AITR-352.pdf?sequence=2. (Some of
his ideas were described in his master’s thesis proposal. See
http://www2.parc.com/spl/members/dekleer/Publications/
QualitativeandQuantitativeKnowledgeinClassicalMechanics.pdf.) [440]
10. Patrick J. Hayes,“The Naive Physics Manifesto,” in D. Michie (ed.), Expert Systems in
the Micro-Electronic Age, pp. 242–270, Edinburgh: Edinburgh University Press, 1979. [440]
11. Patrick J. Hayes, “The Second Naive Physics Manifesto,” in Jerry R. Hobbs and Robert
C. Moore (eds.), Formal Theories of the Commonsense World, pp. 1–36, Norwood, NJ:
Ablex Publishing Corporation, 1985. [440]
12. Patrick J. Hayes, “Naive Physics 1: Ontology for Liquids,” in Jerry R. Hobbs and
Robert C. Moore (eds.), Formal Theories of the Commonsense World, pp. 71–107,
Norwood, NJ: Ablex Publishing Corporation, 1985, An early version appeared as Memo 35
of the Institut pour les Études Semantiques et Cognitives, Université de Genève, 1978. [440]
13. The group’s Web page is at http://www.qrg.northwestern.edu/. [441]
14. The Web page for the Texas group is at http://www.cs.utexas.edu/∼qr/. [441]
15. See, for example, Artificial Intelligence, Vol. 51, Nos. 1–3, October 1991; IEEE Expert:
Intelligent Systems and Their Applications, Vol. 12, No. 3, May/June 1997; the
introductory article by Yumi Iwasaki, “Real World Applications of Qualitative Reasoning:
Introduction to the Special Issue,” AI Magazine, Vol. 24, No. 4, pp. 16–21, Winter 2003 (a
preprint of which is available online at
http://ksl-web.stanford.edu/people/iwasaki/my-intro.ps); Benjamin J. Kuipers, Qualitative
Reasoning: Modeling and Simulation with Incomplete Knowledge, Cambridge, MA: MIT
Press, 1994; and Daniel S. Weld and Johan de Kleer, Readings in Qualitative Reasoning
Copyright c 2010 Nils J. Nilsson
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![26 NOTES
about Physical Systems, San Francisco: Morgan Kaufmann Publishers, 1990. [441]
16. See Ronald J. Brachman and Hector J. Levesque, Knowledge Representation and
Reasoning, San Francisco: Morgan Kaufmann Publishers, 2004. [442]
17. Ronald J. Brachman, “A Structural Paradigm for Representing Knowledge,” Ph.D.
dissertation, Division of Engineering and Applied Physics, Harvard University, Cambridge,
MA, 1977. [442]
18. William A. Woods, “What’s in a Link: Foundations for Semantic Networks,” in Daniel
Bobrow and Allan Collins (eds.), Representation and Understanding: Studies in Cognitive
Science, pp. 35–82, New York: Academic Press, 1975. [443]
19. Ronald J. Brachman and James G. Schmolze, “An Overview of the KL-ONE Knowledge
Representation System,” Cognitive Science: A Multidisciplinary Journal, Vol. 9, No. 2, pp.
171–216, 1985. [443]
20. Ronald J. Brachman, Richard E. Fikes, and Hector J. Levesque, “KRYPTON: A
Functional Approach to Knowledge Representation, IEEE Computer, Vol. 16, No. 10, pp.
67–73, October 1983. Reprinted in Ronald J. Brachman and Hector J. Levesque (eds.),
Readings in Knowledge Representation, pp. 411–429, San Francisco: Morgan Kaufmann
Publishers, 1985. [443]
21. Ibid, pp. 68–69. [443]
22. For a Web site with much information and resources about description logics, see
http://dl.kr.org/. [443]
23. Deborah L. McGuinness, Richard Fikes, Lynn Andrea Stein, and James Hendler,
“DAML-ONT: An Ontology Language for the Semantic Web,” in Dieter Fensel, Jim
Hendler, Henry Lieberman, and Wolfgang Wahlster (eds.), The Semantic Web: Why, What,
and How, Cambridge, MA: MIT Press, 2002; available online at
http://www.ksl.stanford.edu/people/dlm/papers/daml-ont-semantic-web.htm. [443]
24. Deborah L. McGuinness and Frank van Harmelen, “OWL Web Ontology Language
Overview,” W3C Recommendation, February 10, 2004; available online at
http://www.w3.org/TR/owl-features/. [443]
25. Christine Fellbaum (ed.), WordNet: An Electronic Lexical Database, Cambridge, MA:
MIT Press, 1998. See also the WordNet Web site at http://wordnet.princeton.edu/ and the
Wikipedia article at http://en.wikipedia.org/wiki/WordNet. [444]
26. George A. Miller et al., “Introduction to WordNet: An On-line Lexical Database,”
International Journal of Lexicography, Vol. 3, No. 4, pp. 235–244, 1990. [444]
27. The reader interested in details about WordNet might refer to the WordNet Web site
and to the set of five papers appearing in International Journal of Lexicography, Vol. 3, No.
4, 1990. [446]
28. http://en.wikipedia.org/wiki/WordNet. [446]
29. Karin Kipper Schuler, “VerbNet: A Broad-Coverage, Comprehensive Verb Lexicon,”
University of Pennsylvania Ph.D. dissertation, 2005. Online version available from
http://repository.upenn.edu/dissertations/AAI3179808/. [446]
30. http://verbs.colorado.edu/∼mpalmer/projects/verbnet.html. [446]
31. Douglas B. Lenat, “AM: Discovery in Mathematics as Heuristic Search,” in Randall
Davis and Douglas B. Lenat (eds.), Knowledge-Based Systems in Artificial Intelligence, pp.
1–225, New York: McGraw-Hill, 1982; Douglas B. Lenat, “Eurisko: A Program Which
Learns New Heuristics and Domain Concepts,” Artificial Intelligence, Vol. 21, Nos. 1–2,
61–98, 1983. [447]
32. From an article by Lenat in David G. Stork (ed.), Hal’s Legacy: 2001’s Computer as
Dream and Reality, Cambridge, MA: MIT Press, 1998. The article is available online at
452
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![26.3 NOTES
http://www.cyc.com/cyc/technology/halslegacy.html. [447]
33. Ibid. [447]
34. E-mail communication, January 24, 2008. [447]
35. The main paper is Douglas B. Lenat et al., “Cyc: Toward Programs with Common
Sense,” Communications of the ACM, Vol. 33, No. 8, pp. 30–49, August 1990. The first five
years of the Cyc project is described in Douglas B. Lenat and R. V. Guha, Building Large
Knowledge-Based Systems, Reading, MA: Addison-Wesley, 1990. [448]
36. January 13, 2009, e-mail from Doug Lenat. [449]
37. The reader who is interested in more details about how Cyc represents knowledge and
how Cyc reasons can view a set of tutorial slides available online at
http://www.opencyc.org/releases/doc/tut/index html?tree-e=
eJyLLWTUCOVxhAJnIwPbQiYE38Ux2baQOVUPALHZCUs#AAAAAAAADAc=. [449]
38. E-mail communication, January 25, 2008. [450]
39. See http://researchcyc.cyc.com/. [450]
40. See http://opencyc.org/. [450]
41. http://www.cycfoundation.org/concepts. [450]
42. E-mail communication, January 24, 2008. [450]
Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
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all about artificial intelligence
- 1. 0.0 THE QUEST FOR ARTIFICIAL INTELLIGENCE A HISTORY OF IDEAS AND ACHIEVEMENTS Web Version Print version published by Cambridge University Press http://www.cambridge.org/us/0521122937 Nils J. Nilsson Stanford University Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 1
- 2. 0 For Grace McConnell Abbott, my wife and best friend 2 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 3. 0.0 Contents I Beginnings 17 1 Dreams and Dreamers 19 2 Clues 27 2.1 From Philosophy and Logic . . . . . . . . . . . . . . . . . . . . . 27 2.2 From Life Itself . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.2.1 Neurons and the Brain . . . . . . . . . . . . . . . . . . . . 34 2.2.2 Psychology and Cognitive Science . . . . . . . . . . . . . 37 2.2.3 Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.2.4 Development and Maturation . . . . . . . . . . . . . . . . 45 2.2.5 Bionics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 2.3 From Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 2.3.1 Automata, Sensing, and Feedback . . . . . . . . . . . . . 46 2.3.2 Statistics and Probability . . . . . . . . . . . . . . . . . . 52 2.3.3 The Computer . . . . . . . . . . . . . . . . . . . . . . . . 53 II Early Explorations: 1950s and 1960s 71 3 Gatherings 73 3.1 Session on Learning Machines . . . . . . . . . . . . . . . . . . . . 73 3.2 The Dartmouth Summer Project . . . . . . . . . . . . . . . . . . 77 3.3 Mechanization of Thought Processes . . . . . . . . . . . . . . . . 81 4 Pattern Recognition 89 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 3
- 4. 0 CONTENTS 4.1 Character Recognition . . . . . . . . . . . . . . . . . . . . . . . . 90 4.2 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 4.2.1 Perceptrons . . . . . . . . . . . . . . . . . . . . . . . . . . 92 4.2.2 ADALINES and MADALINES . . . . . . . . . . . . . . . 98 4.2.3 The MINOS Systems at SRI . . . . . . . . . . . . . . . . 98 4.3 Statistical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 102 4.4 Applications of Pattern Recognition to Aerial Reconnaissance . . 105 5 Early Heuristic Programs 113 5.1 The Logic Theorist and Heuristic Search . . . . . . . . . . . . . . 113 5.2 Proving Theorems in Geometry . . . . . . . . . . . . . . . . . . . 118 5.3 The General Problem Solver . . . . . . . . . . . . . . . . . . . . . 121 5.4 Game-Playing Programs . . . . . . . . . . . . . . . . . . . . . . . 123 6 Semantic Representations 131 6.1 Solving Geometric Analogy Problems . . . . . . . . . . . . . . . . 131 6.2 Storing Information and Answering Questions . . . . . . . . . . . 134 6.3 Semantic Networks . . . . . . . . . . . . . . . . . . . . . . . . . . 136 7 Natural Language Processing 141 7.1 Linguistic Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 7.2 Machine Translation . . . . . . . . . . . . . . . . . . . . . . . . . 146 7.3 Question Answering . . . . . . . . . . . . . . . . . . . . . . . . . 150 8 1960s’ Infrastructure 155 8.1 Programming Languages . . . . . . . . . . . . . . . . . . . . . . . 155 8.2 Early AI Laboratories . . . . . . . . . . . . . . . . . . . . . . . . 157 8.3 Research Support . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 8.4 All Dressed Up and Places to Go . . . . . . . . . . . . . . . . . . 163 III Efflorescence: Mid-1960s to Mid-1970s 167 9 Computer Vision 169 9.1 Hints from Biology . . . . . . . . . . . . . . . . . . . . . . . . . . 171 4 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 5. 0.0 CONTENTS 9.2 Recognizing Faces . . . . . . . . . . . . . . . . . . . . . . . . . . 172 9.3 Computer Vision of Three-Dimensional Solid Objects . . . . . . 173 9.3.1 An Early Vision System . . . . . . . . . . . . . . . . . . . 173 9.3.2 The “Summer Vision Project” . . . . . . . . . . . . . . . 175 9.3.3 Image Filtering . . . . . . . . . . . . . . . . . . . . . . . . 176 9.3.4 Processing Line Drawings . . . . . . . . . . . . . . . . . . 181 10 “Hand–Eye” Research 189 10.1 At MIT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 10.2 At Stanford . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 10.3 In Japan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 10.4 Edinburgh’s “FREDDY” . . . . . . . . . . . . . . . . . . . . . . . 193 11 Knowledge Representation and Reasoning 199 11.1 Deductions in Symbolic Logic . . . . . . . . . . . . . . . . . . . . 200 11.2 The Situation Calculus . . . . . . . . . . . . . . . . . . . . . . . . 202 11.3 Logic Programming . . . . . . . . . . . . . . . . . . . . . . . . . 203 11.4 Semantic Networks . . . . . . . . . . . . . . . . . . . . . . . . . . 205 11.5 Scripts and Frames . . . . . . . . . . . . . . . . . . . . . . . . . . 207 12 Mobile Robots 213 12.1 Shakey, the SRI Robot . . . . . . . . . . . . . . . . . . . . . . . . 213 12.1.1 A∗ : A New Heuristic Search Method . . . . . . . . . . . . 216 12.1.2 Robust Action Execution . . . . . . . . . . . . . . . . . . 221 12.1.3 STRIPS: A New Planning Method . . . . . . . . . . . . . 222 12.1.4 Learning and Executing Plans . . . . . . . . . . . . . . . 224 12.1.5 Shakey’s Vision Routines . . . . . . . . . . . . . . . . . . 224 12.1.6 Some Experiments with Shakey . . . . . . . . . . . . . . . 228 12.1.7 Shakey Runs into Funding Troubles . . . . . . . . . . . . 229 12.2 The Stanford Cart . . . . . . . . . . . . . . . . . . . . . . . . . . 231 13 Progress in Natural Language Processing 237 13.1 Machine Translation . . . . . . . . . . . . . . . . . . . . . . . . . 237 13.2 Understanding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 5
- 6. 0 CONTENTS 13.2.1 SHRDLU . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 13.2.2 LUNAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 13.2.3 Augmented Transition Networks . . . . . . . . . . . . . . 244 13.2.4 GUS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 14 Game Playing 251 15 The Dendral Project 255 16 Conferences, Books, and Funding 261 IV Applications and Specializations: 1970s to Early 1980s 265 17 Speech Recognition and Understanding Systems 267 17.1 Speech Processing . . . . . . . . . . . . . . . . . . . . . . . . . . 267 17.2 The Speech Understanding Study Group . . . . . . . . . . . . . . 270 17.3 The DARPA Speech Understanding Research Program . . . . . . 271 17.3.1 Work at BBN . . . . . . . . . . . . . . . . . . . . . . . . . 271 17.3.2 Work at CMU . . . . . . . . . . . . . . . . . . . . . . . . 272 17.3.3 Summary and Impact of the SUR Program . . . . . . . . 280 17.4 Subsequent Work in Speech Recognition . . . . . . . . . . . . . . 281 18 Consulting Systems 285 18.1 The SRI Computer-Based Consultant . . . . . . . . . . . . . . . 285 18.2 Expert Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 18.2.1 MYCIN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 18.2.2 PROSPECTOR . . . . . . . . . . . . . . . . . . . . . . . . . 295 18.2.3 Other Expert Systems . . . . . . . . . . . . . . . . . . . . 300 18.2.4 Expert Companies . . . . . . . . . . . . . . . . . . . . . . 303 19 Understanding Queries and Signals 309 19.1 The Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 19.2 Natural Language Access to Computer Systems . . . . . . . . . . 313 19.2.1 LIFER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313 6 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 7. 0.0 CONTENTS 19.2.2 CHAT-80 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 19.2.3 Transportable Natural Language Query Systems . . . . . 318 19.3 HASP/SIAP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 20 Progress in Computer Vision 327 20.1 Beyond Line-Finding . . . . . . . . . . . . . . . . . . . . . . . . . 327 20.1.1 Shape from Shading . . . . . . . . . . . . . . . . . . . . . 327 20.1.2 The 21 2 -D Sketch . . . . . . . . . . . . . . . . . . . . . . . 329 20.1.3 Intrinsic Images . . . . . . . . . . . . . . . . . . . . . . . . 329 20.2 Finding Objects in Scenes . . . . . . . . . . . . . . . . . . . . . . 333 20.2.1 Reasoning about Scenes . . . . . . . . . . . . . . . . . . . 333 20.2.2 Using Templates and Models . . . . . . . . . . . . . . . . 335 20.3 DARPA’s Image Understanding Program . . . . . . . . . . . . . 338 21 Boomtimes 343 V “New-Generation” Projects 347 22 The Japanese Create a Stir 349 22.1 The Fifth-Generation Computer Systems Project . . . . . . . . . 349 22.2 Some Impacts of the Japanese Project . . . . . . . . . . . . . . . 354 22.2.1 The Microelectronics and Computer Technology Corpo- ration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354 22.2.2 The Alvey Program . . . . . . . . . . . . . . . . . . . . . 355 22.2.3 ESPRIT . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355 23 DARPA’s Strategic Computing Program 359 23.1 The Strategic Computing Plan . . . . . . . . . . . . . . . . . . . 359 23.2 Major Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362 23.2.1 The Pilot’s Associate . . . . . . . . . . . . . . . . . . . . . 363 23.2.2 Battle Management Systems . . . . . . . . . . . . . . . . 364 23.2.3 Autonomous Vehicles . . . . . . . . . . . . . . . . . . . . 366 23.3 AI Technology Base . . . . . . . . . . . . . . . . . . . . . . . . . 369 23.3.1 Computer Vision . . . . . . . . . . . . . . . . . . . . . . . 370 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 7
- 8. 0 CONTENTS 23.3.2 Speech Recognition and Natural Language Processing . . 370 23.3.3 Expert Systems . . . . . . . . . . . . . . . . . . . . . . . . 372 23.4 Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 VI Entr’acte 379 24 Speed Bumps 381 24.1 Opinions from Various Onlookers . . . . . . . . . . . . . . . . . . 381 24.1.1 The Mind Is Not a Machine . . . . . . . . . . . . . . . . . 381 24.1.2 The Mind Is Not a Computer . . . . . . . . . . . . . . . . 383 24.1.3 Differences between Brains and Computers . . . . . . . . 392 24.1.4 But Should We? . . . . . . . . . . . . . . . . . . . . . . . 393 24.1.5 Other Opinions . . . . . . . . . . . . . . . . . . . . . . . . 398 24.2 Problems of Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 24.2.1 The Combinatorial Explosion . . . . . . . . . . . . . . . . 399 24.2.2 Complexity Theory . . . . . . . . . . . . . . . . . . . . . . 401 24.2.3 A Sober Assessment . . . . . . . . . . . . . . . . . . . . . 402 24.3 Acknowledged Shortcomings . . . . . . . . . . . . . . . . . . . . . 406 24.4 The “AI Winter” . . . . . . . . . . . . . . . . . . . . . . . . . . . 408 25 Controversies and Alternative Paradigms 413 25.1 About Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413 25.2 Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414 25.3 “Kludginess” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416 25.4 About Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 25.4.1 Behavior-Based Robots . . . . . . . . . . . . . . . . . . . 417 25.4.2 Teleo-Reactive Programs . . . . . . . . . . . . . . . . . . 419 25.5 Brain-Style Computation . . . . . . . . . . . . . . . . . . . . . . 423 25.5.1 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . 423 25.5.2 Dynamical Processes . . . . . . . . . . . . . . . . . . . . . 424 25.6 Simulating Evolution . . . . . . . . . . . . . . . . . . . . . . . . . 425 25.7 Scaling Back AI’s Goals . . . . . . . . . . . . . . . . . . . . . . . 429 8 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 9. 0.0 CONTENTS VII The Growing Armamentarium: From the 1980s Onward 433 26 Reasoning and Representation 435 26.1 Nonmonotonic or Defeasible Reasoning . . . . . . . . . . . . . . . 435 26.2 Qualitative Reasoning . . . . . . . . . . . . . . . . . . . . . . . . 439 26.3 Semantic Networks . . . . . . . . . . . . . . . . . . . . . . . . . . 441 26.3.1 Description Logics . . . . . . . . . . . . . . . . . . . . . . 441 26.3.2 WordNet . . . . . . . . . . . . . . . . . . . . . . . . . . . 444 26.3.3 Cyc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446 27 Other Approaches to Reasoning and Representation 455 27.1 Solving Constraint Satisfaction Problems . . . . . . . . . . . . . 455 27.2 Solving Problems Using Propositional Logic . . . . . . . . . . . . 460 27.2.1 Systematic Methods . . . . . . . . . . . . . . . . . . . . . 461 27.2.2 Local Search Methods . . . . . . . . . . . . . . . . . . . . 463 27.2.3 Applications of SAT Solvers . . . . . . . . . . . . . . . . . 466 27.3 Representing Text as Vectors . . . . . . . . . . . . . . . . . . . . 466 27.4 Latent Semantic Analysis . . . . . . . . . . . . . . . . . . . . . . 469 28 Bayesian Networks 475 28.1 Representing Probabilities in Networks . . . . . . . . . . . . . . . 475 28.2 Automatic Construction of Bayesian Networks . . . . . . . . . . 482 28.3 Probabilistic Relational Models . . . . . . . . . . . . . . . . . . . 486 28.4 Temporal Bayesian Networks . . . . . . . . . . . . . . . . . . . . 488 29 Machine Learning 495 29.1 Memory-Based Learning . . . . . . . . . . . . . . . . . . . . . . . 496 29.2 Case-Based Reasoning . . . . . . . . . . . . . . . . . . . . . . . . 498 29.3 Decision Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 500 29.3.1 Data Mining and Decision Trees . . . . . . . . . . . . . . 500 29.3.2 Constructing Decision Trees . . . . . . . . . . . . . . . . . 502 29.4 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 507 29.4.1 The Backprop Algorithm . . . . . . . . . . . . . . . . . . 508 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 9
- 10. 0 CONTENTS 29.4.2 NETtalk . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509 29.4.3 ALVINN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 510 29.5 Unsupervised Learning . . . . . . . . . . . . . . . . . . . . . . . . 513 29.6 Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . 515 29.6.1 Learning Optimal Policies . . . . . . . . . . . . . . . . . . 515 29.6.2 TD-GAMMON . . . . . . . . . . . . . . . . . . . . . . . . . 522 29.6.3 Other Applications . . . . . . . . . . . . . . . . . . . . . . 523 29.7 Enhancements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 524 30 Natural Languages and Natural Scenes 533 30.1 Natural Language Processing . . . . . . . . . . . . . . . . . . . . 533 30.1.1 Grammars and Parsing Algorithms . . . . . . . . . . . . . 534 30.1.2 Statistical NLP . . . . . . . . . . . . . . . . . . . . . . . . 535 30.2 Computer Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . 539 30.2.1 Recovering Surface and Depth Information . . . . . . . . 541 30.2.2 Tracking Moving Objects . . . . . . . . . . . . . . . . . . 544 30.2.3 Hierarchical Models . . . . . . . . . . . . . . . . . . . . . 548 30.2.4 Image Grammars . . . . . . . . . . . . . . . . . . . . . . . 555 31 Intelligent System Architectures 561 31.1 Computational Architectures . . . . . . . . . . . . . . . . . . . . 563 31.1.1 Three-Layer Architectures . . . . . . . . . . . . . . . . . . 563 31.1.2 Multilayered Architectures . . . . . . . . . . . . . . . . . 563 31.1.3 The BDI Architecture . . . . . . . . . . . . . . . . . . . . 569 31.1.4 Architectures for Groups of Agents . . . . . . . . . . . . . 572 31.2 Cognitive Architectures . . . . . . . . . . . . . . . . . . . . . . . 576 31.2.1 Production Systems . . . . . . . . . . . . . . . . . . . . . 576 31.2.2 ACT-R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 578 31.2.3 SOAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581 VIII Modern AI: Today and Tomorrow 589 32 Extraordinary Achievements 591 10 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 11. 0.0 CONTENTS 32.1 Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 591 32.1.1 Chess . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 591 32.1.2 Checkers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595 32.1.3 Other Games . . . . . . . . . . . . . . . . . . . . . . . . . 598 32.2 Robot Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 600 32.2.1 Remote Agent in Deep Space 1 . . . . . . . . . . . . . . . 600 32.2.2 Driverless Automobiles . . . . . . . . . . . . . . . . . . . . 603 33 Ubiquitous Artificial Intelligence 615 33.1 AI at Home . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 616 33.2 Advanced Driver Assistance Systems . . . . . . . . . . . . . . . . 617 33.3 Route Finding in Maps . . . . . . . . . . . . . . . . . . . . . . . 618 33.4 You Might Also Like. . . . . . . . . . . . . . . . . . . . . . . . . . 618 33.5 Computer Games . . . . . . . . . . . . . . . . . . . . . . . . . . . 619 34 Smart Tools 623 34.1 In Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623 34.2 For Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 625 34.3 For Automated Trading . . . . . . . . . . . . . . . . . . . . . . . 626 34.4 In Business Practices . . . . . . . . . . . . . . . . . . . . . . . . . 627 34.5 In Translating Languages . . . . . . . . . . . . . . . . . . . . . . 628 34.6 For Automating Invention . . . . . . . . . . . . . . . . . . . . . . 628 34.7 For Recognizing Faces . . . . . . . . . . . . . . . . . . . . . . . . 628 35 The Quest Continues 633 35.1 In the Labs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 634 35.1.1 Specialized Systems . . . . . . . . . . . . . . . . . . . . . 634 35.1.2 Broadly Applicable Systems . . . . . . . . . . . . . . . . . 638 35.2 Toward Human-Level Artificial Intelligence . . . . . . . . . . . . 646 35.2.1 Eye on the Prize . . . . . . . . . . . . . . . . . . . . . . . 646 35.2.2 Controversies . . . . . . . . . . . . . . . . . . . . . . . . . 648 35.2.3 How Do We Get It? . . . . . . . . . . . . . . . . . . . . . 649 35.2.4 Some Possible Consequences of HLAI . . . . . . . . . . . 652 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 11
- 12. 0 CONTENTS 35.3 Summing Up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656 12 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 13. 0.0 Preface Artificial intelligence (AI) may lack an agreed-upon definition, but someone writing about its history must have some kind of definition in mind. For me, artificial intelligence is that activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment. According to that definition, lots of things – humans, animals, and some machines – are intelligent. Machines, such as “smart cameras,” and many animals are at the primitive end of the extended continuum along which entities with various degrees of intelligence are arrayed. At the other end are humans, who are able to reason, achieve goals, understand and generate language, perceive and respond to sensory inputs, prove mathematical theorems, play challenging games, synthesize and summarize information, create art and music, and even write histories. Because “functioning appropriately and with foresight” requires so many different capabilities, depending on the environment, we actually have several continua of intelligences with no particularly sharp discontinuities in any of them. For these reasons, I take a rather generous view of what constitutes AI. That means that my history of the subject will, at times, include some control engineering, some electrical engineering, some statistics, some linguistics, some logic, and some computer science. There have been other histories of AI, but time marches on, as has AI, so a new history needs to be written. I have participated in the quest for artificial intelligence for fifty years – all of my professional life and nearly all of the life of the field. I thought it would be a good idea for an “insider” to try to tell the story of this quest from its beginnings up to the present time. I have three kinds of readers in mind. One is the intelligent lay reader interested in scientific topics who might be curious about what AI is all about. Another group, perhaps overlapping the first, consists of those in technical or professional fields who, for one reason or another, need to know about AI and would benefit from a complete picture of the field – where it has been, where it is now, and where it might be going. To both of these groups, I promise no complicated mathematics or computer jargon, lots of diagrams, and my best efforts to provide clear explanations of how AI programs and techniques work. (I also include several photographs of AI people. The selection of these is Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 13
- 14. 0 CONTENTS somewhat random and doesn’t necessarily indicate prominence in the field.) A third group consists of AI researchers, students, and teachers who would benefit from knowing more about the things AI has tried, what has and hasn’t worked, and good sources for historical and other information. Knowing the history of a field is important for those engaged in it. For one thing, many ideas that were explored and then abandoned might now be viable because of improved technological capabilities. For that group, I include extensive end-of-chapter notes citing source material. The general reader will miss nothing by ignoring these notes. The main text itself mentions Web sites where interesting films, demonstrations, and background can be found. (If links to these sites become broken, readers may still be able to access them using the “Wayback Machine” at http://www.archive.org.) The book follows a roughly chronological approach, with some backing and filling. My story may have left out some actors and events, but I hope it is reasonably representative of AI’s main ideas, controversies, successes, and limitations. I focus more on the ideas and their realizations than on the personalities involved. I believe that to appreciate AI’s history, one has to understand, at least in lay terms, something about how AI programs actually work. If AI is about endowing machines with intelligence, what counts as a machine? To many people, a machine is a rather stolid thing. The word evokes images of gears grinding, steam hissing, and steel parts clanking. Nowadays, however, the computer has greatly expanded our notion of what a machine can be. A functioning computer system contains both hardware and software, and we frequently think of the software itself as a “machine.” For example, we refer to “chess-playing machines” and “machines that learn,” when we actually mean the programs that are doing those things. The distinction between hardware and software has become somewhat blurred because most modern computers have some of their programs built right into their hardware circuitry. Whatever abilities and knowledge I bring to the writing of this book stem from the support of many people, institutions, and funding agencies. First, my parents, Walter Alfred Nilsson (1907–1991) and Pauline Glerum Nilsson (1910–1998), launched me into life. They provided the right mixture of disdain for mediocrity and excuses (Walter), kind care (Pauline), and praise and encouragement (both). Stanford University is literally and figuratively my alma mater (Latin for “nourishing mother”). First as a student and later as a faculty member (now emeritus), I have continued to learn and to benefit from colleagues throughout the university and especially from students. SRI International (once called the Stanford Research Institute) provided a home with colleagues who helped me to learn about and to “do” AI. I make special acknowledgement to the late Charles A. Rosen, who persuaded me in 1961 to join his “Learning Machines Group” there. The Defense Advanced Research Projects Agency (DARPA), the Office of Naval Research (ONR), the Air Force 14 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 15. 0.0 CONTENTS Office of Scientific Research (AFOSR), the U.S. Geological Survey (USGS), the National Science Foundation (NSF), and the National Aeronautics and Space Administration (NASA) all supported various research efforts I was part of during the last fifty years. I owe thanks to all. To the many people who have helped me with the actual research and writing for this book, including anonymous and not-so-anonymous reviewers, please accept my sincere appreciation together with my apologies for not naming all of you personally in this preface. There are too many of you to list, and I am afraid I might forget to mention someone who might have made some brief but important suggestions. Anyway, you know who you are. You are many of the people whom I mention in the book itself. However, I do want to mention Heather Bergman, of Cambridge University Press, Mykel Kochenderfer, a former student, and Wolfgang Bibel of the Darmstadt University of Technology. They all read carefully early versions of the entire manuscript and made many helpful suggestions. (Mykel also provided invaluable advice about the L A TEX typesetting program.) I also want to thank the people who invented, developed, and now manage the Internet, the World Wide Web, and the search engines that helped me in writing this book. Using Stanford’s various site licenses, I could locate and access journal articles, archives, and other material without leaving my desk. (I did have to visit libraries to find books. Publishers, please allow copyrighted books, especially those whose sales have now diminished, to be scanned and made available online. Join the twenty-first century!) Finally, and most importantly, I thank my wife, Grace, who cheerfully and patiently urged me on. In 1982, the late Allen Newell, one of the founders of AI, wrote “Ultimately, we will get real histories of Artificial Intelligence. . . , written with as much objectivity as the historians of science can muster. That time is certainly not yet.” Perhaps it is now. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 15
- 16. 0 CONTENTS 16 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 17. 0.0 Part I Beginnings Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 17
- 18. 0 18 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 19. 1.0 Chapter 1 Dreams and Dreamers The quest for artificial intelligence (AI) begins with dreams – as all quests do. People have long imagined machines with human abilities – automata that move and devices that reason. Human-like machines are described in many stories and are pictured in sculptures, paintings, and drawings. You may be familiar with many of these, but let me mention a few. The Iliad of Homer talks about self-propelled chairs called “tripods” and golden “attendants” constructed by Hephaistos, the lame blacksmith god, to help him get around.1∗ And, in the ancient Greek myth as retold by Ovid in his Metamorphoses, Pygmalian sculpts an ivory statue of a beautiful maiden, Galatea, which Venus brings to life:2 The girl felt the kisses he gave, blushed, and, raising her bashful eyes to the light, saw both her lover and the sky. The ancient Greek philosopher Aristotle (384–322 bce) dreamed of automation also, but apparently he thought it an impossible fantasy – thus making slavery necessary if people were to enjoy leisure. In his The Politics, he wrote3 For suppose that every tool we had could perform its task, either at our bidding or itself perceiving the need, and if – like. . . the tripods of Hephaestus, of which the poet [that is, Homer] says that “self-moved they enter the assembly of gods” – shuttles in a loom could fly to and fro and a plucker [the tool used to pluck the strings] play a lyre of their own accord, then master craftsmen would have no need of servants nor masters of slaves. ∗So as not to distract the general reader unnecessarily, numbered notes containing citations to source materials appear at the end of each chapter. Each of these is followed by the number of the page where the reference to the note occurred. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 19
- 20. 1 Dreams and Dreamers Aristotle might have been surprised to see a Jacquard loom weave of itself or a player piano doing its own playing. Pursuing his own visionary dreams, Ramon Llull (circa 1235–1316), a Catalan mystic and poet, produced a set of paper discs called the Ars Magna (Great Art), which was intended, among other things, as a debating tool for winning Muslims to the Christian faith through logic and reason. (See Fig. 1.1.) One of his disc assemblies was inscribed with some of the attributes of God, namely goodness, greatness, eternity, power, wisdom, will, virtue, truth, and glory. Rotating the discs appropriately was supposed to produce answers to various theological questions.4 Figure 1.1: Ramon Llull (left) and his Ars Magna (right). Ahead of his time with inventions (as usual), Leonardo Da Vinci sketched designs for a humanoid robot in the form of a medieval knight around the year 1495. (See Fig. 1.2.) No one knows whether Leonardo or contemporaries tried to build his design. Leonardo’s knight was supposed to be able to sit up, move its arms and head, and open its jaw.5 The Talmud talks about holy persons creating artificial creatures called “golems.” These, like Adam, were usually created from earth. There are stories about rabbis using golems as servants. Like the Sorcerer’s Apprentice, golems were sometimes difficult to control. In 1651, Thomas Hobbes (1588–1679) published his book Leviathan about the social contract and the ideal state. In the introduction Hobbes seems to say that it might be possible to build an “artificial animal.”6 For seeing life is but a motion of limbs, the beginning whereof is in some principal part within, why may we not say that all automata (engines that move themselves by springs and wheels as doth a 20 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 21. 1.0 Figure 1.2: Model of a robot knight based on drawings by Leonardo da Vinci. watch) have an artificial life? For what is the heart, but a spring; and the nerves, but so many strings; and the joints, but so many wheels, giving motion to the whole body. . . Perhaps for this reason, the science historian George Dyson refers to Hobbes as the “patriarch of artificial intelligence.”7 In addition to fictional artifices, several people constructed actual automata that moved in startlingly lifelike ways.8 The most sophisticated of these was the mechanical duck designed and built by the French inventor and engineer, Jacques de Vaucanson (1709–1782). In 1738, Vaucanson displayed his masterpiece, which could quack, flap its wings, paddle, drink water, and eat and “digest” grain. As Vaucanson himself put it,9 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 21
- 22. 1 Dreams and Dreamers My second Machine, or Automaton, is a Duck, in which I represent the Mechanism of the Intestines which are employed in the Operations of Eating, Drinking, and Digestion: Wherein the Working of all the Parts necessary for those Actions is exactly imitated. The Duck stretches out its Neck to take Corn out of your Hand; it swallows it, digests it, and discharges it digested by the usual Passage. There is controversy about whether or not the material “excreted” by the duck came from the corn it swallowed. One of the automates-anciens Web sites10 claims that “In restoring Vaucanson’s duck in 1844, the magician Robert-Houdin discovered that ‘The discharge was prepared in advance: a sort of gruel composed of green-coloured bread crumb . . . ’.” Leaving digestion aside, Vaucanson’s duck was a remarkable piece of engineering. He was quite aware of that himself. He wrote11 I believe that Persons of Skill and Attention, will see how difficult it has been to make so many different moving Parts in this small Automaton; as for Example, to make it rise upon its Legs, and throw its Neck to the Right and Left. They will find the different Changes of the Fulchrum’s or Centers of Motion: they will also see that what sometimes is a Center of Motion for a moveable Part, another Time becomes moveable on that Part, which Part then becomes fix’d. In a Word, they will be sensible of a prodigious Number of Mechanical Combinations. This Machine, when once wound up, performs all its different Operations without being touch’d any more. I forgot to tell you, that the Duck drinks, plays in the Water with his Bill, and makes a gurgling Noise like a real living Duck. In short, I have endeavor’d to make it imitate all the Actions of the living Animal, which I have consider’d very attentively. Unfortunately, only copies of the duck exist. The original was burned in a museum in Nijninovgorod, Russia around 1879. You can watch, ANAS, a modern version, performing at http://www.automates-anciens.com/video 1/ duck automaton vaucanson 500.wmv.12 It is on exhibit in the Museum of Automatons in Grenoble and was designed and built in 1998 by Frédéric Vidoni, a creator in mechanical arts. (See Fig. 1.3.) Returning now to fictional automata, I’ll first mention the mechanical, life-sized doll, Olympia, which sings and dances in Act I of Les Contes d’Hoffmann (The Tales of Hoffmann) by Jacques Offenbach (1819–1880). In the opera, Hoffmann, a poet, falls in love with Olympia, only to be crestfallen (and embarrassed) when she is smashed to pieces by the disgruntled Coppélius. 22 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 23. 1.0 Figure 1.3: Frédéric Vidoni’s ANAS, inspired by Vaucanson’s duck. (Photo- graph courtesy of Frédéric Vidoni.) A play called R.U.R. (Rossum’s Universal Robots) was published by Karel C̆apek (pronounced CHAH pek), a Czech author and playwright, in 1920. (See Fig. 1.4.) C̆apek is credited with coining the word “robot,” which in Czech means “forced labor” or “drudgery.” (A “robotnik” is a peasant or serf.) The play opened in Prague in January 1921. The Robots (always capitalized in the play) are mass-produced at the island factory of Rossum’s Universal Robots using a chemical substitute for protoplasm. According to a Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 23
- 24. 1 Dreams and Dreamers Web site describing the play,13 “Robots remember everything, and think of nothing new. According to Domin [the factory director] ‘They’d make fine university professors.’ . . . once in a while, a Robot will throw down his work and start gnashing his teeth. The human managers treat such an event as evidence of a product defect, but Helena [who wants to liberate the Robots] prefers to interpret it as a sign of the emerging soul.” I won’t reveal the ending except to say that C̆apek did not look eagerly on technology. He believed that work is an essential element of human life. Writing in a 1935 newspaper column (in the third person, which was his habit) he said: “With outright horror, he refuses any responsibility for the thought that machines could take the place of people, or that anything like life, love, or rebellion could ever awaken in their cogwheels. He would regard this somber vision as an unforgivable overvaluation of mechanics or as a severe insult to life.”14 Figure 1.4: A scene from a New York production of R.U.R. There is an interesting story, written by C̆apek himself, about how he came to use the word robot in his play. While the idea for the play “was still warm he rushed immediately to his brother Josef, the painter, who was standing before an easel and painting away. . . . ‘I don’t know what to call these artificial workers,’ he said. ‘I could call them Labori, but that strikes me 24 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 25. 1.0 NOTES as a bit bookish.’ ‘Then call them Robots,’ the painter muttered, brush in mouth, and went on painting.”15 The science fiction (and science fact) writer Isaac Asimov wrote many stories about robots. His first collection, I, Robot, consists of nine stories about “positronic” robots.16 Because he was tired of science fiction stories in which robots (such as Frankenstein’s creation) were destructive, Asimov’s robots had “Three Laws of Robotics” hard-wired into their positronic brains. The three laws were the following: First Law: A robot may not injure a human being, or, through inaction, allow a human being to come to harm. Second Law: A robot must obey the orders given it by human beings except where such orders would conflict with the First Law. Third Law: A robot must protect its own existence as long as such protection does not conflict with the First or Second Law. Asimov later added a “zeroth” law, designed to protect humanity’s interest:17 Zeroth Law: A robot may not injure humanity, or, through inaction, allow humanity to come to harm. The quest for artificial intelligence, quixotic or not, begins with dreams like these. But to turn dreams into reality requires usable clues about how to proceed. Fortunately, there were many such clues, as we shall see. Notes 1. The Iliad of Homer, translated by Richmond Lattimore, p. 386, Chicago: The University of Chicago Press, 1951. (Paperback edition, 1961.) [19] 2. Ovid, Metamorphoses, Book X, pp. 243–297, from an English translation, circa 1850. See http://www.pygmalion.ws/stories/ovid2.htm. [19] 3. Aristotle, The Politics, p. 65, translated by T. A. Sinclair, London: Penguin Books, 1981. [19] 4. See E. Allison Peers, Fool of Love: The Life of Ramon Lull, London: S. C. M. Press, Ltd., 1946. [20] 5. See http://en.wikipedia.org/wiki/Leonardo’s robot. [20] 6. Thomas Hobbes, The Leviathon, paperback edition, Kessinger Publishing, 2004. [20] 7. George B. Dyson, Darwin Among the Machines: The Evolution of Global Intelligence, p. 7, Helix Books, 1997. [21] 8. For a Web site devoted to automata and music boxes, see http://www.automates-anciens.com/english version/frames/english frames.htm. [21] 9. From Jacques de Vaucanson, “An account of the mechanism of an automaton, or image playing on the German-flute: as it was presented in a memoire, to the gentlemen of the Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 25
- 26. 1 NOTES Royal-Academy of Sciences at Paris. By M. Vaucanson . . . Together with a description of an artificial duck. . . . .” Translated out of the French original, by J. T. Desaguliers, London, 1742. Available at http://e3.uci.edu/clients/bjbecker/NatureandArtifice/week5d.html. [21] 10. http://www.automates-anciens.com/english version/automatons-music-boxes/ vaucanson-automatons-androids.php. [22] 11. de Vaucanson, Jacques, op. cit. [22] 12. I thank Prof. Barbara Becker of the University of California at Irvine for telling me about the automates-anciens.com Web sites. [22] 13. http://jerz.setonhill.edu/resources/RUR/index.html. [24] 14. For a translation of the column entitled “The Author of Robots Defends Himself,” see http://www.depauw.edu/sfs/documents/capek68.htm. [24] 15. From one of a group of Web sites about C̆apek, http://Capek.misto.cz/english/robot.html. See also http://Capek.misto.cz/english/. [25] 16. The Isaac Asimov Web site, http://www.asimovonline.com/, claims that “Asimov did not come up with the title, but rather his publisher ‘appropriated’ the title from a short story by Eando Binder that was published in 1939.” [25] 17. See http://www.asimovonline.com/asimov FAQ.html#series13 for information about the history of these four laws. [25] 26 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 27. 2.1 Chapter 2 Clues Clues about what might be needed to make machines intelligent are scattered abundantly throughout philosophy, logic, biology, psychology, statistics, and engineering. With gradually increasing intensity, people set about to exploit clues from these areas in their separate quests to automate some aspects of intelligence. I begin my story by describing some of these clues and how they inspired some of the first achievements in artificial intelligence. 2.1 From Philosophy and Logic Although people had reasoned logically for millennia, it was the Greek philosopher Aristotle who first tried to analyze and codify the process. Aristotle identified a type of reasoning he called the syllogism “. . . in which, certain things being stated, something other than what is stated follows of necessity from their being so.”1 Here is a famous example of one kind of syllogism:2 1. All humans are mortal. (stated) 2. All Greeks are humans. (stated) 3. All Greeks are mortal. (result) The beauty (and importance for AI) of Aristotle’s contribution has to do with the form of the syllogism. We aren’t restricted to talking about humans, Greeks, or mortality. We could just as well be talking about something else – a result made obvious if we rewrite the syllogism using arbitrary symbols in the place of humans, Greeks, and mortal. Rewriting in this way would produce 1. All B’s are A. (stated) Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 27
- 28. 2 Clues 2. All C’s are B’s. (stated) 3. All C’s are A. (result) One can substitute anything one likes for A, B, and C. For example, all athletes are healthy and all soccer players are athletes, and therefore all soccer players are healthy, and so on. (Of course, the “result” won’t necessarily be true unless the things “stated” are. Garbage in, garbage out!) Aristotle’s logic provides two clues to how one might automate reasoning. First, patterns of reasoning, such as syllogisms, can be economically represented as forms or templates. These use generic symbols, which can stand for many different concrete instances. Because they can stand for anything, the symbols themselves are unimportant. Second, after the general symbols are replaced by ones pertaining to a specific problem, one only has to “turn the crank” to get an answer. The use of general symbols and similar kinds of crank-turning are at the heart of all modern AI reasoning programs. In more modern times, Gottfried Wilhelm Leibniz (1646–1716; Fig. 2.1) was among the first to think about logical reasoning. Leibniz was a German philosopher, mathematician, and logician who, among other things, co-invented the calculus. (He had lots of arguments with Isaac Newton about that.) But more importantly for our story, he wanted to mechanize reasoning. Leibniz wrote3 It is unworthy of excellent men to lose hours like slaves in the labor of calculation which could safely be regulated to anyone else if machines were used. and For if praise is given to the men who have determined the number of regular solids. . . how much better will it be to bring under mathematical laws human reasoning, which is the most excellent and useful thing we have. Leibniz conceived of and attempted to design a language in which all human knowledge could be formulated – even philosophical and metaphysical knowledge. He speculated that the propositions that constitute knowledge could be built from a smaller number of primitive ones – just as all words can be built from letters in an alphabetic language. His lingua characteristica or universal language would consist of these primitive propositions, which would comprise an alphabet for human thoughts. The alphabet would serve as the basis for automatic reasoning. His idea was that if the items in the alphabet were represented by numbers, then a 28 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 29. 2.1 From Philosophy and Logic Figure 2.1: Gottfried Leibniz. complex proposition could be obtained from its primitive constituents by multiplying the corresponding numbers together. Further arithmetic operations could then be used to determine whether or not the complex proposition was true or false. This whole process was to be accomplished by a calculus ratiocinator (calculus of reasoning). Then, when philosophers disagreed over some problem they could say, “calculemus” (“let us calculate”). They would first pose the problem in the lingua characteristica and then solve it by “turning the crank” on the calculus ratiocinator. The main problem in applying this idea was discovering the components of the primitive “alphabet.” However, Leibniz’s work provided important additional clues to how reasoning might be mechanized: Invent an alphabet of simple symbols and the means for combining them into more complex expressions. Toward the end of the eighteenth century and the beginning of the nineteenth, a British scientist and politician, Charles Stanhope (Third Earl of Stanhope), built and experimented with devices for solving simple problems in logic and probability. (See Fig. 2.2.) One version of his “box” had slots on the sides into which a person could push colored slides. From a window on the top, one could view slides that were appropriately positioned to represent a Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 29
- 30. 2 Clues specific problem. Today, we would say that Stanhope’s box was a kind of analog computer. Figure 2.2: The Stanhope Square Demonstrator, 1805. (Photograph courtesy of Science Museum/SSPL.) The book Computing Before Computers gives an example of its operation:4 To solve a numerical syllogism, for example: Eight of ten A’s are B’s; Four of ten A’s are C’s; Therefore, at least two B’s are C’s. Stanhope would push the red slide (representing B) eight units across the window (representing A) and the gray slide (representing C) four units from the opposite direction. The two units that the slides overlapped represented the minimum number of B’s that were also C’s. · · · In a similar way the Demonstrator could be used to solve a traditional syllogism like: No M is A; All B is M; Therefore, No B is A. Stanhope was rather secretive about his device and didn’t want anyone to know what he was up to. As mentioned in Computing Before Computers, 30 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 31. 2.1 From Philosophy and Logic “The few friends and relatives who received his privately distributed account of the Demonstrator, The Science of Reasoning Clearly Explained Upon New Principles (1800), were advised to remain silent lest ‘some bastard imitation’ precede his intended publication on the subject.” But no publication appeared until sixty years after Stanhope’s death. Then, the Reverend Robert Harley gained access to Stanhope’s notes and one of his boxes and published an article on what he called “The Stanhope Demonstrator.”5 Contrasted with Llull’s schemes and Leibniz’s hopes, Stanhope built the first logic machine that actually worked – albeit on small problems. Perhaps his work raised confidence that logical reasoning could indeed be mechanized. In 1854, the Englishman George Boole (1815–1864; Fig. 2.3) published a book with the title An Investigation of the Laws of Thought on Which Are Founded the Mathematical Theories of Logic and Probabilities.6 Boole’s purpose was (among other things) “to collect. . . some probable intimations concerning the nature and constitution of the human mind.” Boole considered various logical principles of human reasoning and represented them in mathematical form. For example, his “Proposition IV” states “. . . the principle of contradiction. . . affirms that it is impossible for any being to possess a quality, and at the same time not to possess it. . . .” Boole then wrote this principle as an algebraic equation, x(1 − x) = 0, in which x represents “any class of objects,” (1 − x) represents the “contrary or supplementary class of objects,” and 0 represents a class that “does not exist.” In Boolean algebra, an outgrowth of Boole’s work, we would say that 0 represents falsehood, and 1 represents truth. Two of the fundamental operations in logic, namely OR and AND, are represented in Boolean algebra by the operations + and ×, respectively. Thus, for example, to represent the statement “either p or q or both,” we would write p + q. To represent the statement “p and q,” we would write p × q. Each of p and q could be true or false, so we would evaluate the value (truth or falsity) of p + q and p × q by using definitions for how + and × are used, namely, 1 + 0 = 1, 1 × 0 = 0, 1 + 1 = 1, 1 × 1 = 1, 0 + 0 = 0, and 0 × 0 = 0. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 31
- 32. 2 Clues Figure 2.3: George Boole. Boolean algebra plays an important role in the design of telephone switching circuits and computers. Although Boole probably could not have envisioned computers, he did realize the importance of his work. In a letter dated January 2, 1851, to George Thomson (later Lord Kelvin) he wrote7 I am now about to set seriously to work upon preparing for the press an account of my theory of Logic and Probabilities which in its present state I look upon as the most valuable if not the only valuable contribution that I have made or am likely to make to Science and the thing by which I would desire if at all to be remembered hereafter. . . Boole’s work showed that some kinds of logical reasoning could be performed by manipulating equations representing logical propositions – a very important clue about the mechanization of reasoning. An essentially equivalent, but not algebraic, system for manipulating and evaluating propositions is called the “propositional calculus” (often called “propositional logic”), which, as we shall see, plays a very important role in artificial intelligence. [Some claim that the Greek Stoic philospher Chrysippus (280–209 bce) invented an early form of the propositional calculus.8 ] 32 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 33. 2.2 From Life Itself One shortcoming of Boole’s logical system, however, was that his propositions p, q, and so on were “atomic.” They don’t reveal any entities internal to propositions. For example, if we expressed the proposition “Jack is human” by p, and “Jack is mortal” by q, there is nothing in p or q to indicate that the Jack who is human is the very same Jack who is mortal. For that, we need, so to speak, “molecular expressions” that have internal elements. Toward the end of the nineteenth century, the German mathematician, logician, and philosopher Friedrich Ludwig Gottlob Frege (1848–1925) invented a system in which propositions, along with their internal components, could be written down in a kind of graphical form. He called his language Begriffsschrift, which can be translated as “concept writing.” For example, the statement “All persons are mortal” would have been written in Begriffsschrift something like the diagram in Fig. 2.4.9 Figure 2.4: Expressing “All persons are mortal” in Begriffsschrift. Note that the illustration explicitly represents the x who is predicated to be a person and that it is the same x who is then claimed to be mortal. It’s more convenient nowadays for us to represent this statement in the linear form (∀x)P(x)⊃M(x), whose English equivalent is “for all x, if x is a person, then x is mortal.” Frege’s system was the forerunner of what we now call the “predicate calculus,” another important system in artificial intelligence. It also foreshadows another representational form used in present-day artificial intelligence: semantic networks. Frege’s work provided yet more clues about how to mechanize reasoning processes. At last, sentences expressing information to be reasoned about could be written in unambiguous, symbolic form. 2.2 From Life Itself In Proverbs 6:6–8, King Solomon says “Go to the ant, thou sluggard; consider her ways and be wise.” Although his advice was meant to warn against slothfulness, it can just as appropriately enjoin us to seek clues from biology about how to build or improve artifacts. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 33
- 34. 2 Clues Several aspects of “life” have, in fact, provided important clues about intelligence. Because it is the brain of an animal that is responsible for converting sensory information into action, it is to be expected that several good ideas can be found in the work of neurophysiologists and neuroanatomists who study brains and their fundamental components, neurons. Other ideas are provided by the work of psychologists who study (in various ways) intelligent behavior as it is actually happening. And because, after all, it is evolutionary processes that have produced intelligent life, those processes too provide important hints about how to proceed. 2.2.1 Neurons and the Brain In the late nineteenth and early twentieth centuries, the “neuron doctrine” specified that living cells called “neurons” together with their interconnections were fundamental to what the brain does. One of the people responsible for this suggestion was the Spanish neuroanatomist Santiago Ramón y Cajal (1852–1934). Cajal (Fig. 2.5) and Camillo Golgi won the Nobel Prize in Physiology or Medicine in 1906 for their work on the structure of the nervous system. A neuron is a living cell, and the human brain has about ten billion (1010 ) of them. Although they come in different forms, typically they consist of a central part called a soma or cell body, incoming fibers called dendrites, and one or more outgoing fibers called axons. The axon of one neuron has projections called terminal buttons that come very close to one or more of the dendrites of other neurons. The gap between the terminal button of one neuron and a dendrite of another is called a synapse. The size of the gap is about 20 nanometers. Two neurons are illustrated schematically in Fig. 2.6. Through electrochemical action, a neuron may send out a stream of pulses down its axon. When a pulse arrives at the synapse adjacent to a dendrite of another neuron, it may act to excite or to inhibit electrochemical activity of the other neuron across the synapse. Whether or not this second neuron then “fires” and sends out pulses of its own depends on how many and what kinds of pulses (excitatory or inhibitory) arrive at the synapses of its various incoming dendrites and on the efficiency of those synapses in transmitting electrochemical activity. It is estimated that there are over half a trillion synapses in the human brain. The neuron doctrine claims that the various activities of the brain, including perception and thinking, are the result of all of this neural activity. In 1943, the American neurophysiologist Warren McCulloch (1899–1969; Fig. 2.7) and logician Walter Pitts (1923–1969) claimed that the neuron was, in essence, a “logic unit.” In a famous and important paper they proposed simple models of neurons and showed that networks of these models could perform all possible computational operations.10 The McCulloch–Pitts “neuron” was a mathematical abstraction with inputs and outputs 34 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 35. 2.2 From Life Itself Figure 2.5: Ramón y Cajal. (corresponding, roughly, to dendrites and axons, respectively). Each output can have the value 1 or 0. (To avoid confusing a McCulloch–Pitts neuron with a real neuron, I’ll call the McCulloch–Pitts version, and others like it, a “neural element.”) The neural elements can be connected together into networks such that the output of one neural element is an input to others and so on. Some neural elements are excitatory – their outputs contribute to “firing” any neural elements to which they are connected. Others are inhibitory – their outputs contribute to inhibiting the firing of neural elements to which they are connected. If the sum of the excitatory inputs less the sum of the inhibitory inputs impinging on a neural element is greater than a certain “threshold,” that neural element fires, sending its output of 1 to all of the neural elements to which it is connected. Some examples of networks proposed by McCullough and Pitts are shown in Fig. 2.8. The Canadian neuropsychologist Donald O. Hebb (1904–1985) also Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 35
- 36. 2 Clues Figure 2.6: Two neurons. (Adapted from Science, Vol. 316, p. 1416, 8 June 2007. Used with permission.) believed that neurons in the brain were the basic units of thought. In an influential book,11 Hebb suggested that “when an axon of cell A is near enough to excite B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased.” Later, this so-called Hebb rule of change in neural “synaptic strength” was actually observed in experiments with living animals. (In 1965, the neurophysiologist Eric Kandel published results showing that simple forms of learning were associated with synaptic changes in the marine mollusk Aplysia californica. In 2000, Kandel shared the Nobel Prize in Physiology or Medicine “for their discoveries concerning signal transduction in the nervous system.”) Hebb also postulated that groups of neurons that tend to fire together formed what he called cell assemblies. Hebb thought that the phenomenon of “firing together” tended to persist in the brain and was the brain’s way of representing the perceptual event that led to a cell-assembly’s formation. Hebb said that “thinking” was the sequential activation of sets of cell assemblies.12 36 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 37. 2.2 From Life Itself Figure 2.7: Warren McCulloch. 2.2.2 Psychology and Cognitive Science Psychology is the science that studies mental processes and behavior. The word is derived from the Greek words psyche, meaning breath, spirit, or soul, and logos, meaning word. One might expect that such a science ought to have much to say that would be of interest to those wanting to create intelligent artifacts. However, until the late nineteenth century, most psychological theorizing depended on the insights of philosophers, writers, and other astute observers of the human scene. (Shakespeare, Tolstoy, and other authors were no slouches when it came to understanding human behavior.) Most people regard serious scientific study to have begun with the Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 37
- 38. 2 Clues Figure 2.8: Networks of McCulloch–Pitts neural elements. (Adapted from Fig. 1 of Warren S. McCulloch and Walter Pitts, “A Logical Calculus of Ideas Im- manent in Nervous Activity,” Bulletin of Mathematical Biophysics, Vol. 5, pp. 115–133, 1943.) German Wilhelm Wundt (1832–1920) and the American William James (1842–1910).13 Both established psychology labs in 1875 – Wundt in Leipzig and James at Harvard. According to C. George Boeree, who teaches the history of psychology at Shippensburg University in Pennsylvania, “The method that Wundt developed is a sort of experimental introspection: The 38 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 39. 2.2 From Life Itself researcher was to carefully observe some simple event – one that could be measured as to quality, intensity, or duration – and record his responses to variations of those events.” Although James is now regarded mainly as a philosopher, he is famous for his two-volume book The Principles of Psychology, published in 1873 and 1874. Both Wundt and James attempted to say something about how the brain worked instead of merely cataloging its input–output behavior. The psychiatrist Sigmund Freud (1856–1939) went further, postulating internal components of the brain, namely, the id, the ego, and the superego, and how they interacted to affect behavior. He thought one could learn about these components through his unique style of guided introspection called psychoanalysis. Attempting to make psychology more scientific and less dependent on subjective introspection, a number of psychologists, most famously B. F. Skinner (1904–1990; Fig. 2.9), began to concentrate solely on what could be objectively measured, namely, specific behavior in reaction to specific stimuli. The behaviorists argued that psychology should be a science of behavior, not of the mind. They rejected the idea of trying to identify internal mental states such as beliefs, intentions, desires, and goals. Figure 2.9: B. F. Skinner. (Photograph courtesy of the B. F. Skinner Founda- tion.) Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 39
- 40. 2 Clues This development might at first be regarded as a step backward for people wanting to get useful clues about the internal workings of the brain. In criticizing the statistically oriented theories arising from “behaviorism,” Marvin Minsky wrote “Originally intended to avoid the need for ‘meaning,’ [these theories] manage finally only to avoid the possibility of explaining it.”14 Skinner’s work did, however, provide the idea of a reinforcing stimulus – one that rewards recent behavior and tends to make it more likely to occur (under similar circumstances) in the future. Reinforcement learning has become a popular strategy among AI researchers, although it does depend on internal states. Russell Kirsch (circa 1930– ), a computer scientist at the U.S. National Bureau of Standards (now the National Institute for Standards and Technology, NIST), was one of the first to use it. He proposed how an “artificial animal” might use reinforcement to learn good moves in a game. In some 1954 seminar notes he wrote the following:15 “The animal model notes, for each stimulus, what move the opponent next makes, . . . Then, the next time that same stimulus occurs, the animal duplicates the move of the opponent that followed the same stimulus previously. The more the opponent repeats the same move after any given stimulus, the more the animal model becomes ‘conditioned’ to that move.” Skinner believed that reinforcement learning could even be used to explain verbal behavior in humans. He set forth these ideas in his 1957 book Verbal Behavior,16 claiming that the laboratory-based principles of selection by consequences can be extended to account for what people say, write, gesture, and think. Arguing against Skinner’s ideas about language the linguist Noam Chomsky (1928– ; Fig. 2.10), in a review17 of Skinner’s book, wrote that careful study of this book (and of the research on which it draws) reveals, however, that [Skinner’s] astonishing claims are far from justified. . . . the insights that have been achieved in the laboratories of the reinforcement theorist, though quite genuine, can be applied to complex human behavior only in the most gross and superficial way, and that speculative attempts to discuss linguistic behavior in these terms alone omit from consideration factors of fundamental importance. . . How, Chomsky seems to ask, can a person produce a potentially infinite variety of previously unheard and unspoken sentences having arbitrarily complex structure (as indeed they can do) through experience alone? These “factors of fundamental importance” that Skinner omits are, according to Chomsky, linguistic abilities that must be innate – not learned. He suggested that “human beings are somehow specially created to do this, with data-handling or ‘hypothesis-formulating’ ability of [as yet] unknown character and complexity.” Chomsky claimed that all humans have at birth a “universal 40 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 41. 2.2 From Life Itself Figure 2.10: Noam Chomsky. (Photograph by Don J. Usner.) grammar” (or developmental mechanisms for creating one) that accounts for much of their ability to learn and use languages.18 Continuing the focus on internal mental processes and their limitations, the psychologist George A. Miller (1920– ) analyzed the work of several experimenters and concluded that the “immediate memory” capacity of humans was approximately seven “chunks” of information.19 In the introduction to his paper about this “magical number,” Miller humorously notes “My problem is that I have been persecuted by an integer. For seven years this number has followed me around, has intruded in my most private data, and has assaulted me from the pages of our most public journals. This number assumes a variety of disguises, being sometimes a little larger and sometimes a little smaller than usual, but never changing so much as to be unrecognizable. The persistence with which this number plagues me is far more than a random accident.” Importantly, he also claimed that “the span of immediate memory seems to be almost independent of the number of bits per chunk.” That is, it doesn’t matter what a chunk represents, be it a single digit in a phone number, a name of a person just mentioned, or a song title; we can apparently only hold seven of them (plus or minus two) in our immediate Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 41
- 42. 2 Clues memory. Miller’s paper on “The Magical Number Seven,” was given at a Symposium on Information Theory held from September 10 to 12, 1956, at MIT.20 Chomsky presented an important paper there too. It was entitled “Three Models for the Description of Language,” and in it he proposed a family of rules of syntax he called phrase-structure grammars.21 It happens that two pioneers in AI research (of whom we’ll hear a lot more later), Allen Newell (1927–1992), then a scientist at the Rand Corporation, and Herbert Simon (1916–2001), a professor at the Carnegie Institute of Technology (now Carnegie Mellon University), gave a paper there also on a computer program that could prove theorems in propositional logic. This symposium, bringing together as it did scientists with these sorts of overlapping interests, is thought to have contributed to the birth of cognitive science, a new discipline devoted to the study of the mind. Indeed, George Miller wrote22 I went away from the Symposium with a strong conviction, more intuitive than rational, that human experimental psychology, theoretical linguistics, and computer simulation of cognitive processes were all pieces of a larger whole, and that the future would see progressive elaboration and coordination of their shared concerns. . . In 1960, Miller and colleagues wrote a book proposing a specific internal mechanism responsible for behavior, which they called the TOTE unit (Test–Operate–Test–Exit).23 There is a TOTE unit corresponding to every goal that an agent might have. Using its perceptual abilities, the unit first tests whether or not its goal is satisfied. If so, the unit rests (exits). If not, some operation specific to achieving that goal is performed, and the test for goal achievement is performed again, and so on repetitively until the goal finally is achieved. As a simple example, consider the TOTE unit for driving a nail with a hammer. So long as the nail is not completely driven in (the goal), the hammer is used to strike it (the operation). Pounding stops (the exit) when the goal is finally achieved. It’s difficult to say whether or not this book inspired similar work by artificial intelligence researchers. The idea was apparently “in the air,” because at about the same time, as we shall see later, some early work in AI used very similar ideas. [I can say that my work at SRI with behavior (intermediate-level) programs for the robot, Shakey, and my later work on what I called “teleo-reactive” programs were influenced by Miller’s ideas.] Cognitive science attempted to explicate internal mental processes using ideas such as goals, memory, task queues, and strategies without (at least during its beginning years) necessarily trying to ground these processes in neurophysiology.24 Cognitive science and artificial intelligence have been closely related ever since their beginnings. Cognitive science has provided clues for AI researchers, and AI has helped cognitive science with newly 42 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 43. 2.2 From Life Itself invented concepts useful for understanding the workings of the mind. 2.2.3 Evolution That living things evolve gives us two more clues about how to build intelligent artifacts. First, and most ambitiously, the processes of evolution itself – namely, random generation and selective survival – might be simulated on computers to produce the machines we dream about. Second, those paths that evolution followed in producing increasingly intelligent animals can be used as a guide for creating increasingly intelligent artifacts. Start by simulating animals with simple tropisms and proceed along these paths to simulating more complex ones. Both of these strategies have been followed with zest by AI researchers, as we shall see in the following chapters. Here, it will suffice to name just a few initial efforts. Early attempts to simulate evolution on a computer were undertaken at Princeton’s Institute for Advanced Study by the viral geneticist Nils Aall Barricelli (1912–1993). His 1954 paper described experiments in which numbers migrated and reproduced in a grid.25 Motivated by the success of biological evolution in producing complex organisms, some researchers began thinking about how programs could be evolved rather than written. R. N. Friedberg and his IBM colleagues26 conducted experiments in which, beginning with a population of random computer programs, they attempted to evolve ones that were more successful at performing a simple logical task. In the summary of his 1958 paper, Friedberg wrote that “[m]achines would be more useful if they could learn to perform tasks for which they were not given precise methods. . . . It is proposed that the program of a stored-program computer be gradually improved by a learning procedure which tries many programs and chooses, from the instructions that may occupy a given location, the one most often associated with a successful result.” That is, Friedberg installed instructions from “successful” programs into the programs of the next “generation,” much as how the genes of individuals successful enough to have descendants are installed in those descendants. Unfortunately, Friedberg’s attempts to evolve programs were not very successful. As Marvin Minsky pointed out,27 The machine [described in the first paper] did learn to solve some extremely simple problems. But it took of the order of 1000 times longer than pure chance would expect. . . . The second paper goes on to discuss a sequence of modifications. . . With these, and with some ‘priming’ (starting the machine off on the right track with some useful instructions), the system came to be only a little worse than chance. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 43
- 44. 2 Clues Minsky attributes the poor performance of Friedberg’s methods to the fact that each descendant machine differed very little from its parent, whereas any helpful improvement would require a much larger step in the “space” of possible machines. Other early work on artificial evolution was more successful. Lawrence Fogel (1928–2007) and colleagues were able to evolve machines that could make predictions of the next element in a sequence.28 Woodrow W. Bledsoe (1921–1995) at Panoramic Research and Hans J. Bremermann (1926–1969) at the University of California, Berkeley, used simulated evolution to solve optimization and mathematical problems, respectively.29 And Ingo Rechenberg (according to one AI researcher) “pioneered the method of artificial evolution to solve complex optimization tasks, such as the design of optimal airplane wings or combustion chambers of rocket nozzles.”30 The first prominent work inspired by biological evolution was John Holland’s development of “genetic algorithms” beginning in the early 1960s. Holland (1929– ), a professor at the University of Michigan, used strings of binary symbols (0’s and 1’s), which he called “chromosomes” in analogy with the genetic material of biological organisms. (Holland says he first came up with the notion while browsing through the Michigan math library’s open stacks in the early 1950s.)31 The encoding of 0’s and 1’s in a chromosome could be interpreted as a solution to some given problem. The idea was to evolve chromosomes that were better and better at solving the problem. Populations of chromosomes were subjected to an evolutionary process in which individual chromosomes underwent “mutations” (changing a component 1 to a 0 and vice versa), and pairs of the most successful chromosomes at each stage of evolution were combined to make a new chromosome. Ultimately, the process would produce a population containing a chromosome (or chromosomes) that solved the problem.32 Researchers would ultimately come to recognize that all of these evolutionary methods were elaborations of a very useful mathematical search strategy called “gradient ascent” or “hill climbing.” In these methods, one searches for a local maximum of some function by taking the steepest possible uphill steps. (When searching for a local minimum, the analogous method is called “gradient descent.”) Rather than attempt to duplicate evolution itself, some researchers preferred to build machines that followed along evolution’s paths toward intelligent life. In the late 1940s and early 1950s, W. Grey Walter (1910–1977), a British neurophysiologist (born in Kansas City, Missouri), built some machines that behaved like some of life’s most primitive creatures. They were wheeled vehicles to which he gave the taxonomic name Machina speculatrix (machine that looks; see Fig. 2.11).33 These tortoise-like machines were controlled by “brains” consisting of very simple vacuum-tube circuits that sensed their environments with photocells and that controlled their wheel motors. The circuits could be arranged so that a machine either moved toward 44 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 45. 2.2 From Life Itself or away from a light mounted on a sister machine. Their behaviors seemed purposive and often complex and unpredictable, so much so that Walter said they “might be accepted as evidence of some degree of self-awareness.” Machina speculatrix was the beginning of a long line of increasingly sophisticated “behaving machines” developed by subsequent researchers. Figure 2.11: Grey Walter (top left), his Machina speculatrix (top right), and its circuit diagram (bottom). (Grey Walter photograph from Hans Moravec, ROBOT, Chapter 2: Caution! Robot Vehicle!, p. 18, Oxford: Oxford University Press, 1998; “Turtle” photograph courtesy of National Museum of American History, Smithsonian Institution; the circuit diagram is from W. Grey Walter, The Living Brain, p. 200, London: Gerald Duckworth & Co., Ltd., 1953.) 2.2.4 Development and Maturation Perhaps there are alternatives to rerunning evolution itself or to following its paths toward increasing complexity from the most primitive animals. By careful study of the behavior of young children, the Swiss psychologist Jean Piaget proposed a set of stages in the maturation of their thinking abilities Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 45
- 46. 2 Clues from infancy to adolescence.34 Might these stages provide a set of steps that could guide designers of intelligent artifacts? Start with a machine that is able to do what an infant can do, and then design machines that can mimic the abilities of children at each rung of the ladder. This strategy might be called “ontogenetic” to contrast it with the “phylogenetic” strategy of using simlulated evolution. Of course, it may be that an infant mind is far too complicated to simulate and the processes of its maturation too difficult to follow. In any case, this particular clue remains to be exploited. 2.2.5 Bionics At a symposium in 1960, Major Jack E. Steele, of the Aerospace Division of the United States Air Force, used the term “bionics” to describe the field that learns lessons from nature to apply to technology.35 Several bionics and bionics-related meetings were held during the 1960s. At the 1963 Bionics Symposium, Leonard Butsch and Hans Oestreicher wrote “Bionics aims to take advantage of millions of years of evolution of living systems during which they adapted themselves for optimum survival. One of the outstanding successes of evolution is the information processing capability of living systems [the study of which is] one of the principal areas of Bionics research.”36 Today, the word “bionics” is concerned mainly with orthotic and prosthetic devices, such as artificial cochleas, retinas, and limbs. Nevertheless, as AI researchers continue their quest, the study of living things, their evolution, and their development may continue to provide useful clues for building intelligent artifacts. 2.3 From Engineering 2.3.1 Automata, Sensing, and Feedback Machines that move by themselves and even do useful things by themselves have been around for centuries. Perhaps the most common early examples are the “verge-and-foliot” weight-driven clocks. (See Fig. 2.12.) These first appeared in the late Middle Ages in the towers of large Italian cities. The verge-and-foliot mechanism converted the energy of a falling weight into stepped rotational motion, which could be used to move the clock hands. Similar mechanisms were elaborated to control the actions of automata, such as those of the Munich Glockenspiel. One of the first automatic machines for producing goods was Joseph-Marie Jacquard’s weaving loom, built in 1804. (See Fig. 2.13.) It 46 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 47. 2.3 From Engineering Figure 2.12: A verge-and-foliot mechanism (left) and automata at the Munich Glockenspiel (right). followed a long history of looms and improved on the “punched card” design of Jacques de Vaucanson’s loom of 1745. (Vaucanson did more than build mechanical ducks.) The punched cards of the Jacquard loom controlled the actions of the shuttles, allowing automatic production of fabric designs. Just a few years after its invention, there were some 10,000 Jacquard looms weaving away in France. The idea of using holes in paper or cards was later adopted by Herman Hollerith for tabulating the 1890 American census data and in player pianos (using perforated rolls instead of cards). The very first factory “robots” of the so-called pick-and-place variety used only modest elaborations of this idea. It was only necessary to provide these early machines with an external source of energy (a falling weight, a wound-up spring, or humans pumping pedals). Their behavior was otherwise fully automatic, requiring no human guidance. But, they had an important limitation – they did not perceive anything about their environments. (The punched cards that were “read” by the Jacquard loom are considered part of the machine – not part of the environment.) Sensing the environment and then letting what is sensed influence what a machine does is critical to intelligent behavior. Grey Walters’s “tortoises,” for example, had photocells that could detect the presence or absence of light in their environments and act accordingly. Thus, they seem more intelligent than a Jacquard loom or clockwork automata. One of the simplest ways to allow what is sensed to influence behavior involves what is called “feedback control.” The word derives from feeding some aspect of a machine’s behavior, say its speed of operation, back into the Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 47
- 48. 2 Clues Figure 2.13: Reconstruction of a Jacquard loom. internals of the machine. If the aspect of behavior that is fed back acts to diminish or reverse that aspect, the process is called “negative feedback.” If, on the other hand, it acts to increase or accentuate that aspect of behavior, it is called “positive feedback.” Both types of feedback play extremely important roles in engineering. 48 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 49. 2.3 From Engineering Negative feedback techniques have been used for centuries in mechanical devices. In 270 bce, a Greek inventor and barber, Ktesibios of Alexandria, invented a float regulator to keep the water level in a tank feeding a water clock at a constant depth by controlling the water flow into the tank.37 The feedback device was a float valve consisting of a cork at the end of a rod. The cork floated on the water in the tank. When the water level in the tank rose, the cork would rise, causing the rod to turn off the water coming in. When the water level fell, the cork would fall, causing the rod to turn on the water. The water level in modern flush toilets is regulated in much the same way. In 250 bce, Philon of Byzantium used a similar float regulator to keep a constant level of oil in a lamp.38 The English clockmaker John Harrison (1693–1776) used a type of negative feedback control in his clocks. The ambient temperature of a clock affects the length of its balance spring and thus its time-keeping accuracy. Harrison used a bimetallic strip (sometimes a rod), whose curvature depends on temperature. The strip was connected to the balance spring in such a way that it produced offsetting changes in the length of the spring, thus making the clock more independent of its temperature. The strip senses the temperature and causes the clock to behave differently, and more accurately, than it otherwise would. Today, such bimetallic strips see many uses, notably in thermostats. (Dava Sobel’s 1995 book, Longitude: The True Story of a Lone Genius Who Solved the Greatest Scientific Problem of His Time, recounts the history of Harrison’s efforts to build a prize-winning clock for accurate time-keeping at sea.) Perhaps the most graphic use of feedback control is the centrifugal flyball governor perfected in 1788 by James Watt for regulating the speed of his steam engine. (See Fig. 2.14.) As the speed of the engine increases, the balls fly outward, which causes a linking mechanism to decrease air flow, which causes the speed to decrease, which causes the balls to fall back inward, which causes the speed to increase, and so on, resulting in an equilibrium speed. In the early 1940s, Norbert Wiener (1894–1964) and other scientists noted similarities between the properties of feedback control systems in machines and in animals. In particular, inappropriately applied feedback in control circuits led to jerky movements of the system being controlled that were similar to pathological “tremor” in human patients. Arturo Rosenblueth, Norbert Wiener, and Julian Bigelow coined the term “cybernetics” in a 1943 paper. Wiener’s book by that name was published in 1948. The word is related to the word “governor.” (In Latin gubernaculum means helm, and gubernator means helmsman. The Latin derives from the Greek kybernetike, which means the art of steersmanship.39 ) Today, the prefix “cyber” is used to describe almost anything that deals with computers, robots, the Internet, and advanced simulation. For example, the author William Gibson coined the term “cyberspace” in his 1984 science fiction novel Neuromancer. Technically, however, cybernetics continues to Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 49
- 50. 2 Clues Figure 2.14: Watt’s flyball governor. describe activities related to feedback and control.40 The English psychiatrist W. Ross Ashby (1903–1972; Fig. 2.15) contributed to the field of cybernetics by his study of “ultrastability” and “homeostasis.” According to Ashby, ultrastability is the capacity of a system to reach a stable state under a wide variety of environmental conditions. To illustrate the idea, he built an electromechanical device called the “homeostat.” It consisted of four pivoted magnets whose positions were rendered interdependent through feedback mechanisms. If the position of any was disturbed, the effects on the others and then back on itself would result in all of them returning to an equilibrium condition. Ashby described this device in Chapter 8 of his influential 1952 book Design For a Brain. His ideas had an influence on several AI researchers. My “teleo-reactive programs,” to be 50 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 51. 2.3 From Engineering described later, were motivated in part by the idea of homeostasis. Figure 2.15: W. Ross Ashby, Warren McCulloch, Grey Walter, and Norbert Wiener at a Meeting in Paris. (From P. de Latil, Thinking by Machine: A Study of Cybernetics, Boston: Houghton, Mifflin, 1957.) Another source of ideas, loosely associated with cybernetics and bionics, came from studies of “self-organizing systems.” Many unorganized combinations of simple parts, including combinations of atoms and molecules, respond to energetic “jostling” by falling into stable states in which the parts are organized in more complex assemblies. An online dictionary devoted to cybernetics and systems theory has a nice example: “A chain made out of paper clips suggests that someone has taken the trouble to link paper clips together to make a chain. It is not in the nature of paper clips to make themselves up into a chain. But, if you take a number of paper clips, open them up slightly and then shake them all together in a cocktail shaker, you will find at the end that the clips have organized themselves into short or long chains. The chains are not so neat as chains put together by hand but, nevertheless, they are chains.”41 The term “self-organizing” seems to have been first introduced by Ashby in 1947.42 Ashby emphasized that self-organization is not a property of an organism itself, in response to its environment and experience, but a property of the organism and its environment taken together. Although self-organization appears to be important in ideas about how life originated, it is unclear whether or not it provides clues for building intelligent machines. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 51
- 52. 2 Clues 2.3.2 Statistics and Probability Because nearly all reasoning and decision making take place in the presence of uncertainty, dealing with uncertainty plays an important role in the automation of intelligence. Attempts to quantify uncertainty and “the laws of chance” gave rise to statistics and probability theory. What would turn out to be one of the most important results in probability theory, at least for artificial intelligence, is Bayes’s rule, which I’ll define presently in the context of an example. The rule is named for Reverend Thomas Bayes (1702–1761), an English clergyman.43 One of the important applications of Bayes’s rule is in signal detection. Let’s suppose a radio receiver is tuned to a station that after midnight broadcasts (randomly) one of two tones, either tone A or tone B, and on a particular night we want to decide which one is being broadcast. On any given day, we do not know ahead of time which tone is to be broadcast that night, but suppose we do know their probabilities. (For example, it might be that both tones are equally probable.) Can we find out which tone is being broadcast by listening to the signal coming in to the receiver? Well, listening can’t completely resolve the matter because the station is far away, and random noise partially obscures the tone. However, depending on the nature of the obscuring noise, we can often calculate the probability that the actual tone that night is A (or that it is B). Let’s call the signal y and the actual tone x (which can be either A or B). The probability that x = A, given the evidence for it contained in the incoming signal, y, is written as p(x = A | y) and read as “the probability that x is A, given that the signal is y.” The probability that x = B, given the same evidence is p(x = B | y). A reasonable “decision rule” would be to decide in favor of tone A if p(x = A | y) is larger than p(x = B | y). Otherwise, decide in favor of tone B. (There is a straightforward adjustment to this rule that takes into account differences in the “costs” of the two possible errors.) The problem in applying this rule is that these two probabilities are not readily calculable, and that is where Bayes’s rule comes in. It allows us to calculate these probabilities in terms of other probabilities that are more easily guessed or otherwise obtainable. Specifically, Bayes’s rule is p(x | y) = p(y | x)p(x)/p(y). Using Bayes’s rule, our decision rule can now be reformulated as Decide in favor of tone A if p(y | x = A)p(x = A)/p(y) is greater than p(y | x = B)p(x = B)/p(y). Otherwise, decide in favor of tone B. Because p(y) occurs in both expressions and therefore does not affect which one is larger, the rule simplifies to 52 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 53. 2.3 From Engineering Decide in favor of tone A if p(y | x = A)p(x = A) is greater than p(y | x = B)p(x = B). Otherwise, decide in favor of tone B. We assume that we know the a priori probabilities of the tones, namely, p(x = A) and p(x = B), so it remains only for us to calculate p(y | x) for x = A and x = B. This expression is called the likelihood of y given x. When the two probabilities, p(x = A) and p(x = B), are equal (that is, when both tones are equally probable a priori), then we can decide in favor of which likelihood is greater. Many decisions that are made in the presence of uncertainty use this “maximum-likelihood” method . The calculation for these likelihoods depends on how we represent the received signal, y, and on the statistics of the interfering noise. In my example, y is a radio signal, that is, a voltage varying in time. For computational purposes, this time-varying voltage can be represented by a sequence of samples of its values at appropriately chosen, uniformly spaced time points, say y(t1), y(t2), . . . y(ti), . . . , y(tN ). When noise alters these values from what they would have been without noise, the probability of the sequence of them (given the cases when the tone is A and when the tone is B) can be calculated by using the known statistical properties of the noise. I won’t go into the details here except to say that, for many types of noise statistics, these calculations are quite straightforward. In the twentieth century, scientists and statisticians such as Karl Pearson (1857–1936), Sir Ronald A. Fisher (1890–1962), Abraham Wald (1902–1950), and Jerzey Neyman (1894–1981) were among those who made important contributions to the use of statistical and probabilistic methods in estimating parameters and in making decisions. Their work set the foundation for some of the first engineering applications of Bayes’s rule, such as the one I just illustrated, namely, deciding which, if any, of two or more electrical signals is present in situations where noise acts to obscure the signals. A paper by the American engineers David Van Meter and David Middleton, which I read as a beginning graduate student in 1955, was my own introduction to these applications.44 For artificial intelligence, these uses of Bayes’s rule provided clues about how to mechanize the perception of both speech sounds and visual images. Beyond perception, Bayes’s rule lies at the center of much other modern work in artificial intelligence. 2.3.3 The Computer A. Early Computational Devices Proposals such as those of Leibniz, Boole, and Frege can be thought of as early attempts to provide foundations for what would become the “software” of artificial intelligence. But reasoning and all the other aspects of intelligent behavior require, besides software, some sort of physical engine. In humans Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 53
- 54. 2 Clues and other animals, that engine is the brain. The simple devices of Grey Walter and Ross Ashby were, of course, physical manifestations of their ideas. And, as we shall see, early networks of neuron-like units were realized in physical form. However, to explore the ideas inherent in most of the clues from logic, from neurophysiology, and from cognitive science, more powerful engines would be required. While McCulloch, Wiener, Walter, Ashby, and others were speculating about the machinery of intelligence, a very powerful and essential machine bloomed into existence – the general-purpose digital computer. This single machine provided the engine for all of these ideas and more. It is by far the dominant hardware engine for automating intelligence. Building devices to compute has a long history. William Aspray has edited an excellent book, Computing Before Computers, about computing’s early days.45 The first machines were able to do arithmetic calculations, but these were not programmable. Wilhelm Schickard (1592–1635; Fig. 2.16) built one of the first of these in 1623. It is said to have been able to add and subtract six-digit numbers for use in calculating astronomical tables. The machine could “carry” from one digit to the next. In 1642 Blaise Pascal (1623–1662; Fig. 2.16) created the first of about fifty of his computing machines. It was an adding machine that could perform automatic carries from one position to the next. “The device was contained in a box that was small enough to fit easily on top of a desk or small table. The upper surface of the box. . . consisted of a number of toothed wheels, above which were a series of small windows to show the results. In order to add a number, say 3, to the result register, it was only necessary to insert a small stylus into the toothed wheel at the position marked 3 and rotate the wheel clockwise until the stylus encountered the fixed stop. . . ”46 Figure 2.16: Wilhelm Schickard (left) and Blaise Pascal (right). 54 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 55. 2.3 From Engineering Inspired by Pascal’s machines, Gottfried Leibniz built a mechanical multiplier called the “Step Reckoner” in 1674. It could add, subtract, and do multiplication (by repeated additions). “To multiply a number by 5, one simply turned the crank five times.”47 Several other calculators were built in the ensuing centuries. A particularly interesting one, which was too complicated to build in its day, was designed in 1822 by Charles Babbage (1791–1871), an English mathematician and inventor. (See Fig. 2.17.) Called the “Difference Engine,” it was to have calculated mathematical tables (of the kind used in navigation at sea, for example) using the method of finite differences. Babbage’s Difference Engine No. 2 was actually constructed in 1991 (using Babbage’s designs and nineteenth-century mechanical tolerances) and is now on display at the London Science Museum. The Museum arranged for another copy to be built for Nathan Myhrvold, a former Microsoft Chief Technology Officer. (A description of the machine and a movie is available from a Computer History Museum Web page at http://www.computerhistory.org/babbage/.) Adding machines, however, can only add and subtract (and, by repetition of these operations, also multiply and divide). These are important operations but not the only ones needed. Between 1834 and 1837 Babbage worked on the design of a machine called the “Analytical Engine,” which embodied most of the ideas needed for general computation. It could store intermediate results in a “mill,” and it could be programmed. However, its proposed realization as a collection of steam-driven, interacting brass gears and cams ran into funding difficulties and was never constructed. Ada Lovelace (1815–1852), the daughter of Lord Byron, has been called the “world’s first programmer” for her alleged role in devising programs for the Analytical Engine. However, in the book Computing Before Computers the following claim is made:48 This romantically appealing image is without foundation. All but one of the programs cited in her notes [to her translation of an account of a lecture Babbage gave in Turin, Italy] had been prepared by Babbage from three to seven years earlier. The exception was prepared by Babbage for her, although she did detect a “bug” in it. Not only is there no evidence that Ada Lovelace ever prepared a program for the Analytical Engine but her correspondence with Babbage shows that she did not have the knowledge to do so. For more information about the Analytical Engine and an emulator and programs for it, see http://www.fourmilab.ch/babbage/. Practical computers had to await the invention of electrical, rather than brass, devices. The first computers in the early 1940s used electromechanical relays. Vacuum tubes (thermionic valves, as they say in Britain) soon won out Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 55
- 56. 2 Clues Figure 2.17: Charles Babbage (left) and a model of his Analytical Engine (right). because they permitted faster and more reliable computation. Nowadays, computers use billions of tiny transistors arrayed on silicon wafers. Who knows what might someday replace them? B. Computation Theory Even before people actually started building computers, several logicians and mathematicians in the 1930s pondered the problem of just what could be computed. Alonzo Church came up with a class of functions that could be computed, ones he called “recursive.”49 The English logician and mathematician, Alan Turing (1912–1954; Fig. 2.18), proposed what is now understood to be an equivalent class – ones that could be computed by an imagined machine he called a “logical computing machine (LCM),” nowadays called a “Turing machine.”50 (See Fig. 2.19.) The claim that these two notions are equivalent is called the “Church–Turing Thesis.” (The claim has not been proven, but it is strongly supported by logicians and no counterexample has ever been found.)51 The Turing machine is a hypothetical computational device that is quite simple to understand. It consists of just a few parts. There is an infinite tape (which is one reason the device is just imagined and not actually built) divided into cells and a tape drive. Each cell has printed on it either a 1 or a 0. The machine also has a read–write head positioned over one cell of the tape. The read function reads what is on the tape. There is also a logic unit that can decide, depending on what is read and the state of the logic machine, to change its own state, to command the write function to write either a 1 or a 0 on the 56 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 57. 2.3 From Engineering Figure 2.18: Alan Mathison Turing. (Photograph by Elliott & Fry c and used with permission of the National Portrait Gallery, London.) cell being read (possibly replacing what is already there), to move the tape one cell to the left or to the right (at which time the new cell is read and so on), or to terminate operation altogether. The input (the “problem” to be computed) is written on the tape initially. (It turns out that any such input can be coded into 1’s and 0’s.) When, and if, the machine terminates, the output (the coded “answer” to the input problem) ends up being printed on the tape. Turing proved that one could always specify a particular logic unit (the part that decides on the machine’s actions) for his machine such that the machine would compute any computable function. More importantly, he showed that one could encode on the tape itself a prescription for any logic unit specialized for a particular problem and then use a general-purpose logic unit for all problems. The encoding for the special-purpose logic unit can be thought of as the “program” for the machine, which is stored on the tape (and thus subject to change by the very operation of the machine!) along with the description of the problem to be solved. In Turing’s words, “It can be shown that a single special machine of that type can be made to do the work of all. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 57
- 58. 2 Clues Figure 2.19: A Turing machine. It could in fact be made to work as a model of any other machine. The special machine may be called the universal machine.”52 C. Digital Computers Somewhat independently of Turing, engineers began thinking about how to build actual computing devices consisting of programs and logical circuitry for performing the instructions contained in the programs. Some of the key ideas for designing the logic circuits of computers were developed by the American mathematician and inventor Claude Shannon (1916–2001; Fig. 2.20).53 In his 1937 Yale University master’s thesis54 Shannon showed that Boolean algebra and binary arithmetic could be used to simplify telephone switching circuits. He also showed that switching circuits (which can be realized either by combinations of relays, vacuum tubes, or whatever) could be used to implement operations in Boolean logic, thus explaining their importance in computer design. It’s hard to know who first thought of the idea of storing a computer’s program along with its data in the computer’s memory banks. Storing the program allows changes in the program to be made easily, but more 58 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 59. 2.3 From Engineering Figure 2.20: Claude Shannon. (Photograph courtesy of MIT Museum.) importantly it allows the program to change itself by changing appropriate parts of the memory where the program is stored. Among those who might have thought of this idea first are the German engineer Konrad Zuse (1910–1995) and the American computer pioneers J. Presper Eckert (1919–1995) and John W. Mauchly (1907–1980). (Of course Turing had already proposed storing what amounted to a program on the tape of a universal Turing machine.) For an interesting history of Konrad Zuse’s contributions, see the family of sites available from http://irb.cs.tu-berlin.de/∼zuse/Konrad Zuse/en/index.html. One of these mentions that “it is undisputed that Konrad Zuse’s Z3 was the first fully functional, program controlled (freely programmable) computer of the world. . . . The Z3 was presented on May 12, 1941, to an audience of scientists in Berlin.” Instead of vacuum tubes, it used 2,400 electromechanical relays. The original Z3 was destroyed by an Allied air raid on December 21, 1943.55 A Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 59
- 60. 2 Clues reconstructed version was built in the early 1960s and is now on display at the Deutsche Museum in Munich. Zuse also is said to have created the first programming language, called the Plankalkül. The American mathematician John von Neumann (1903–1957) wrote a “draft report” about the EDVAC, an early stored-program computer.56 Perhaps because of this report, we now say that these kinds of computers use a “von Neumann architecture.” The ideal von Neumann architecture separates the (task-specific) stored program from the (general-purpose) hardware circuitry, which can execute (sequentially) the instructions of any program whatsoever. (We usually call the program “software” to distinguish it from the “hardware” part of a computer. However, the distinction is blurred in most modern computers because they often have some of their programs built right into their circuitry.) Other computers with stored programs were designed and built in the 1940s in Germany, Great Britain, and the United States. They were large, bulky machines. In Great Britain and the United States they were mainly used for military purposes. Figure 2.21 shows one such machine. Figure 2.21: The Cambridge University EDSAC computer (circa 1949). (Pho- tograph used with permission of the Computer Laboratory, University of Cam- bridge c .) 60 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 61. 2.3 From Engineering We call computers “machines” even though today they can be made completely electrical with no moving parts whatsoever. Furthermore, when we speak of computing machines we usually mean the combination of the computer and the program it is running. Sometimes we even call just the program a machine. (As an example of this usage, I’ll talk later about a “checker-playing machine” and mean a program that plays checkers.) The commanding importance of the stored-program digital computer derives from the fact that it can be used for any purpose whatsoever – that is, of course, any computational purpose. The modern digital computer is, for all practical purposes, such a universal machine. The “all-practical-purposes” qualifier is needed because not even modern computers have the infinite storage capacity implied by Turing’s infinite tape. However, they do have prodigious amounts of storage, and that makes them practically universal. D. “Thinking” Computers After some of the first computers were built, Turing reasoned that if they were practically universal, they should be able to do anything. In 1948 he wrote, “The importance of the universal machine is clear. We do not need to have an infinity of different machines doing different jobs. A single one will suffice. The engineering problem of producing various machines for various jobs is replaced by the office work of ‘programming’ the universal machine to do these jobs.”57 Among the things that Turing thought could be done by computers was mimicking human intelligence. One of Turing’s biographers, Andrew Hodges, claims, “he decided the scope of the computable encompassed far more than could be captured by explicit instruction notes, and quite enough to include all that human brains did, however creative or original. Machines of sufficient complexity would have the capacity for evolving into behaviour that had never been explicitly programmed.”58 The first modern article dealing with the possibility of mechanizing all of human-style intelligence was published by Turing in 1950.59 This paper is famous for several reasons. First, Turing thought that the question “Can a machine think?” was too ambiguous. Instead he proposed that the matter of machine intelligence be settled by what has come to be called “the Turing test.” Although there have been several reformulations (mostly simplifications) of the test, here is how Turing himself described it: The new form of the problem [Can machines think?] can be described in terms of a game which we call the “imitation game.” It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart from the other two. The object of the game for the Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 61
- 62. 2 Clues interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either “X is A and Y is B” or “X is B and Y is A.” The interrogator is allowed to put questions to A and B thus: C: Will X please tell me the length of his or her hair? Now suppose X is actually A, then A must answer. It is A’s object in the game to try and cause C to make the wrong identification. His answer might therefore be “My hair is shingled, and the longest strands are about nine inches long.” In order that tones of voice may not help the interrogator the answers should be written, or better still, typewritten. The ideal arrangement is to have a teleprinter communicating between the two rooms. Alternatively the question and answers can be repeated by an intermediary. The object of the game for the third player (B) is to help the interrogator. The best strategy for her is probably to give truthful answers. She can add such things as “I am the woman, don’t listen to him!” to her answers, but it will avail nothing as the man can make similar remarks. We now ask the question, “What will happen when a machine takes the part of A in this game?” Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, “Can machines think?” Most of the simplifications of Turing’s test involve attempting to have a computer “in the other room” fool an interrogator into believing the computer is a human. To stimulate progress toward that goal, a prize has been established – annual prizes in fact. In the words of the Web site announcing the prizes, “In 1990 Hugh Loebner agreed with The Cambridge Center for Behavioral Studies to underwrite a contest designed to implement the Turing Test. Dr. Loebner pledged a Grand Prize of $100,000 and a Gold Medal for the first computer whose responses were indistinguishable from a human’s. Such a computer can be said ‘to think.’ Each year an annual prize of $2000 and a bronze medal is awarded to the most human-like computer. The winner of the annual contest is the best entry relative to other entries that year, irrespective of how good it is in an absolute sense.”60 However, several people have pointed to inadequacies of both the simplifications and even of the original test.61 In fact, it has been alleged that even Joseph Weizenbaum’s patently unintelligent 1966 conversational program, ELIZA, has fooled people into thinking it is human.62 For example, here is part of a transcript of a conversation with ELIZA: 62 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 63. 2.3 From Engineering ELIZA: How do you do. Please state your problem. > I spend too much time with computers. ELIZA: Why do you mention computers? > You are one. ELIZA: Does it please you to believe I am one? > Yes. ELIZA: You seem to be quite positive. > I suppose I am. ELIZA: How long have you been? > 31 years. ELIZA: I’m not sure I understand you fully. > Join the club. ELIZA: Please go on. > You don’t make too much sense yourself. ELIZA: We were discussing you – not me. The first part of the conversation seems reasonable, but ELIZA bogs down in the middle because the program was expecting “I suppose I am” to be followed by some word like “happy.” (There are several ELIZA simulations on the Web. One that claims to reproduce faithfully the original ELIZA program is at http://www.chayden.net/eliza/Eliza.html. Try one out!) A second important feature of Turing’s 1950 paper was his handling of arguments that people might raise against the possibility of achieving intelligent computers. I’ll quote the ones Turing mentions: (1) The Theological Objection: Thinking is a function of man’s immortal soul. God has given an immortal soul to every man and woman, but not to any other animal or to machines. Hence no animal or machine can think. (2) The ‘Heads in the Sand’ Objection: “The consequences of machines thinking would be too dreadful. Let us hope and believe that they cannot do so.” (3) The Mathematical Objection: There are a number of results of mathematical logic that can be used to show that there are limitations to the powers of discrete-state machines. (4) The Argument from Consciousness: This argument is very well expressed in Professor Jefferson’s Lister Oration for 1949, from which I quote: “Not until a machine can write a sonnet or compose a concerto because of thoughts and emotions felt, and not by the chance fall Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 63
- 64. 2 Clues of symbols, could we agree that machine equals brain – that is, not only write it but know that it had written it. No mechanism could feel (and not merely artificially signal, an easy contrivance) pleasure at its successes, grief when its valves fuse, be warmed by flattery, be made miserable by its mistakes, be charmed by sex, be angry or depressed when it cannot get what it wants.” (5) Arguments from Various Disabilities: These arguments take the form, “I grant you that you can make machines do all the things you have mentioned but you will never be able to make one to do X.” (6) Lady Lovelace’s Objection: Our most detailed information of Babbage’s Analytical Engine comes from a memoir by Lady Lovelace. In it she states, “The Analytical Engine has no pretensions to originate anything. It can do whatever we know how to order it to perform” (her italics). (7) Argument from Continuity in the Nervous System: The nervous system is certainly not a discrete-state machine. A small error in the information about the size of a nervous impulse impinging on a neuron may make a large difference to the size of the outgoing impulse. It may be argued that, this being so, one cannot expect to be able to mimic the behavior of the nervous system with a discrete-state system. (8) The Argument from Informality of Behavior: It is not possible to produce a set of rules purporting to describe what a man should do in every conceivable set of circumstances. (9) The Argument from Extra-Sensory Perception. In his paper, Turing nicely (in my opinion) handles all of these points, with the possible exception of the last one (because he apparently thought that extra-sensory perception was plausible). I’ll leave it to you to read Turing’s 1950 paper to see his counterarguments. The third important feature of Turing’s 1950 paper is his suggestion about how we might go about producing programs with human-level intellectual abilities. Toward the end of his paper, he suggests, “Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child’s? If this were then subjected to an appropriate course of education one would obtain the adult brain.” This suggestion is really the source for the idea mentioned earlier about using an ontogenetic strategy to develop intelligent machines. Allen Newell and Herb Simon (see Fig. 2.22) were among those who had no trouble believing that the digital computer’s universality meant that it could be used to mechanize intelligence in all its manifestations – provided it had the right software. In their 1975 ACM Turing Award lecture,63 they 64 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 65. 2.3 From Engineering described a hypothesis that they had undoubtedly come to believe much earlier, the “Physical Symbol System Hypothesis.” It states that “a physical symbol system has the necessary and sufficient means for intelligent action.” Therefore, according to the hypothesis, appropriately programmed digital computers would be capable of intelligent action. Conversely, because humans are capable of intelligent action, they must be, according to the hypothesis, physical symbol systems. These are very strong claims that continue to be debated. Figure 2.22: Herbert Simon (seated) and Allen Newell (standing). (Courtesy of Carnegie Mellon University Archives.) Both the imagined Turing machine and the very real digital computer are symbol systems in the sense Newell and Simon meant the phrase. How can a Turing machine, which uses a tape with 0’s and 1’s printed on it, be a “symbol system”? Well, the 0’s and 1’s printed on the tape can be thought of as symbols standing for their associated numbers. Other symbols, such as “A” and “M,” can be encoded as sequences of primitive symbols, such as 0’s and 1’s. Words can be encoded as sequences of letters, and so on. The fact that one commonly thinks of a digital computer as a machine operating on 0’s and 1’s need not prevent us from thinking of it also as operating on more complex symbols. After all, we are all used to using computers to do “word processing” and to send e-mail. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 65
- 66. 2 NOTES Newell and Simon admitted that their hypothesis could indeed be false: “Intelligent behavior is not so easy to produce that any system will exhibit it willy-nilly. Indeed, there are people whose analyses lead them to conclude either on philosophical or on scientific grounds that the hypothesis is false. Scientifically, one can attack or defend it only by bringing forth empirical evidence about the natural world.” They conclude the following: The symbol system hypothesis implies that the symbolic behavior of man arises because he has the characteristics of a physical symbol system. Hence, the results of efforts to model human behavior with symbol systems become an important part of the evidence for the hypothesis, and research in artificial intelligence goes on in close collaboration with research in information processing psychology, as it is usually called. Although the hypothesis was not formally described until it appeared in the 1976 article, it was certainly implicit in what Turing and other researchers believed in the 1950s. After Allen Newell’s death, Herb Simon wrote, “From the very beginning something like the physical symbol system hypothesis was embedded in the research.”64 Inspired by the clues we have mentioned and armed with the general-purpose digital computer, researchers began, during the 1950s, to explore various paths toward mechanizing intelligence. With a firm belief in the symbol system hypothesis, some people began programming computers to attempt to get them to perform some of the intellectual tasks that humans could perform. Around the same time, other researchers began exploring approaches that did not depend explicitly on symbol processing. They took their inspiration mainly from the work of McCulloch and Pitts on networks of neuron-like units and from statistical approaches to decision making. A split between symbol-processing methods and what has come to be called “brain-style” and “nonsymbolic” methods still survives today. Notes 1. Aristotle, Prior Analytics, Book I, written circa 350 bce, translated by A. J. Jenkinson, Web addition published by eBooks@Adelaide, available online at http://etext.library.adelaide.edu.au/a/aristotle/a8pra/. [27] 2. Medieval students of logic gave names to the different syllogisms they studied. They used the mnemonic Barbara for this one because each of the three statements begins with “All,” whose first letter is “A.” The vowels in “Barbara” are three“a”s. [27] 3. From Martin Davis, The Universal Computer: The Road from Leibniz to Turing, New York: W. W. Norton & Co., 2000. For an excerpt from the paperback version containing this quotation, see http://www.wwnorton.com/catalog/fall01/032229EXCERPT.htm. [28] 4. Quotation from William Aspray (ed.), Computing Before Computers, Chapter 3, “Logic 66 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 67. 2.3 NOTES Machines,” pp. 107–8, Ames, Iowa: Iowa State Press, 1990. (Also available from http://ed-thelen.org/comp-hist/CBC.html.) [30] 5. Robert Harley, “The Stanhope Demonstrator, Mind, Vol. IV, pp. 192–210, 1879. [31] 6. George Boole, An Investigation of the Laws of Thought on Which are Founded the Mathematical Theories of Logic and Probabilities, Dover Publications, 1854. [31] 7. See D. McHale, George Boole: His Life and Work, Dublin, 1985. This excerpt was taken from http://www-groups.dcs.st-and.ac.uk/∼history/Mathematicians/Boole.html. [32] 8. See, for example, Gerard O’Regan, A Brief History of Computing, p. 17, London: Springer-Verlag, 2008. [32] 9. I follow the pictorial version used in the online Stanford Encyclopedia of Philosophy (http://plato.stanford.edu/entries/frege/), which states that “. . . we are modifying Frege’s notation a bit so as to simplify the presentation; we shall not use the special typeface (Gothic) that Frege used for variables in general statements, or observe some of the special conventions that he adopted. . . .” [33] 10. Warren S. McCulloch and Walter Pitts, “A Logical Calculus of Ideas Immanent in Nervous Activity,” Bulletin of Mathematical Biophysics, Vol. 5, pp. 115–133, Chicago: University of Chicago Press, 1943. (See Marvin Minsky, Computation: Finite and Infinite Machines, Englewood Cliffs, NJ: Prentice-Hall, 1967, for a very readable treatment of the computational aspects of “McCulloch–Pitts neurons.”) [34] 11. Donald O. Hebb, The Organization of Behavior: A Neuropsychological Theory, New York: John Wiley, Inc., 1949. [36] 12. For more about Hebb, see http://www.cpa.ca/Psynopsis/special eng.html. [36] 13. For a summary of the lives and work of both men, see a Web page entitled “Wilhelm Wundt and William James” by Dr. C. George Boeree at http://www.ship.edu/∼cgboeree/wundtjames.html. [38] 14. M. Minsky (ed.), “Introduction,” Semantic Information Processing, p. 2, Cambridge, MA: MIT Press, 1968. [40] 15. Russell A. Kirsch, “Experiments with a Computer Learning Routine,” Computer Seminar Notes, July 30, 1954. Available online at http://www.nist.gov/msidlibrary/doc/kirsch 1954 artificial.pdf. [40] 16. B. F. Skinner, Verbal Behavior, Engelwood Cliffs, NJ: Prentice Hall, 1957. [40] 17. Noam Chomsky, “A Review of B. F. Skinner’s Verbal Behavior,” in Leon A. Jakobovits and Murray S. Miron (eds.), Readings in the Psychology of Language, Engelwood Cliffs, NJ: Prentice-Hall, 1967. Available online at http://www.chomsky.info/articles/1967----.htm. [40] 18. See, for example, N. Chomsky, Aspects of the Theory of Syntax, Cambridge: MIT Press, 1965. [41] 19. George A. Miller, “The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information,” The Psychological Review, Vol. 63, pp. 81–97, 1956. [41] 20. IRE Transactions on Information Theory, Vol IT-2, 1956. [42] 21. For a copy of his paper, see http://www.chomsky.info/articles/195609--.pdf. [42] 22. George A. Miller, “A Very Personal History,” MIT Center for Cognitive Science Occasional Paper No. 1, 1979. [42] 23. George A. Miller, E. Galanter, and K. H. Pribram, Plans and the Structure of Behavior, New York: Holt, Rinehart & Winston, 1960. [42] 24. For a thorough history of cognitive science, see Margaret A. Boden, Mind As Machine: Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 67
- 68. 2 NOTES A History of Cognitive Science, vols. 1 and 2, Oxford: Clarendon Press, 2006. For an earlier, one-volume treatment, see Howard E. Gardner, The Mind’s New Science: A History of the Cognitive Revolution, New York: Basic Books, 1985. [42] 25. An English translation appeared later: N.A. Barricelli, “Symbiogenetic Evolution Processes Realized by Artificial Methods,” Methodos, Vol. 9, Nos. 35–36, pp. 143–182, 1957. For a summary of Barricelli’s experiments, see David B. Fogel, “Nils Barricelli – Artificial Life, Coevolution, Self-Adaptation,” IEEE Computational Intelligence Magazine, Vol. 1, No. 1, pp. 41–45, February 2006. [43] 26. R. M. Friedberg, “A Learning Machine: Part I,” IBM Journal of Research and Development, Vol. 2, No. 1, pp. 2–13, 1958, and R. M. Friedberg, B. Dunham, and J. H. North, “A Learning Machine: Part II,” IBM Journal of Research and Development, Vol. 3, No. 3, pp. 282–287, 1959. The papers are available (for a fee) at http://www.research.ibm.com/journal/rd/021/ibmrd0201B.pdf and http://www.research.ibm.com/journal/rd/033/ibmrd0303H.pdf. [43] 27. Marvin L. Minsky, “Steps Toward Artificial Intelligence,” Proceedings of the Institute of Radio Engineers, Vol. 49, pp. 8–30, 1961. Paper available at http://web.media.mit.edu/∼minsky/papers/steps.html. [43] 28. Lawrence J. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence through Simulated Evolution, New York: Wiley, 1966. [44] 29. Woodrow W. Bledsoe, “The Evolutionary Method in Hill Climbing: Convergence Rates,” Technical Report, Panoramic Research, Inc., Palo Alto, CA, 1962.; Hans J. Bremermann, “Optimization through Evolution and Recombination, M. C. Yovits, G. T. Jacobi, and G. D. Goldstein (eds.), Self-Organizing Systems, pp. 93–106, Washington, DC: Spartan Books, 1962. [44] 30. Jürgen Schmidhuber, “2006: Celebrating 75 Years of AI – History and Outlook: The Next 25 Years,” in Max Lungarella et al. (eds.), 50 Years of Artificial Intelligence: Essays Dedicated to the 50th Anniversary of Artificial Intelligence, Berlin: Springer-Verlag, 2007. Schmiduber cites Ingo Rechenberg, “Evolutionsstrategie – Optimierung Technischer Systeme Nach Prinzipien der Biologischen Evolution,” Ph.D. dissertation, 1971 (reprinted by Frommann-Holzboog Verlag, Stuttgart, 1973). [44] 31. See http://www.aaai.org/AITopics/html/genalg.html. [44] 32. John H. Holland, Adaptation in Natural and Artificial Systems, Ann Arbor: The University of Michigan Press, 1975. Second edition, MIT Press, 1992. [44] 33. W. Grey Walter, “An Imitation of Life,” Scientific American, pp. 42–45, May 1950. See also W. Grey Walter, The Living Brain, London: Gerald Duckworth & Co. Ltd., 1953. [44] 34. B. Inhelder and J. Piaget, The Growth of Logical Thinking from Childhood to Adolescence, New York: Basic Books, 1958. For a summary of these stages, see the following Web pages: http://www.childdevelopmentinfo.com/development/piaget.shtml and http://www.ship.edu/∼cgboeree/piaget.html. [46] 35. Proceedings of the Bionics Symposium: Living Prototypes – the Key to new Technology, Technical Report 60-600, Wright Air Development Division, Dayton, Ohio, 1960. [46] 36. Proceedings of the Third Bionics Symposium, Aerospace Medical Division, Air Force Systems Command, United States Air Force, Wright-Patterson AFB, Ohio, 1963. [46] 37. http://www.mlahanas.de/Greeks/Ctesibius1.htm. [49] 38. http://www.asc-cybernetics.org/foundations/timeline.htm. [49] 39. From http://www.nickgreen.pwp.blueyonder.co.uk/control.htm. [49] 40. For a history of cybernetics, see a Web page of the American Society for Cybernetics at http://www.asc-cybernetics.org/foundations/history.htm. [50] 68 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 69. 2.3 NOTES 41. From http://pespmc1.vub.ac.be/ASC/SELF-ORGANI.html. [51] 42. W. Ross Ashby, “Principles of the Self-Organizing Dynamic System,” Journal of General Psychology, Vol. 37, pp. 125–128, 1947. See also the Web pages at http://en.wikipedia.org/wiki/Self organization. [51] 43. Bayes wrote an essay that is said to have contained a version of the rule. Later, the Marquis de Laplace (1749–1827) generalized (some say independently) what Bayes had done. For a version of Bayes’s essay (posthumously written up by Richard Price), see http://www.stat.ucla.edu/history/essay.pdf. [52] 44. David Van Meter and David Middleton, “Modern Statistical Approaches to Reception in Communication Theory,” Symposium on Information Theory, IRE Transactions on Information Theory, PGIT-4, pp. 119–145, September 1954. [53] 45. William Aspray (ed.), Computing Before Computers, Ames, Iowa: Iowa State University Press, 1990. Available online at http://ed-thelen.org/comp-hist/CBC.html. [54] 46. Ibid, Chapter 1. [54] 47. Ibid. [55] 48. Ibid, Chapter 2. [55] 49. Alonzo Church, “An Unsolvable Problem of Elementary Number Theory,” American Journal of Mathematics, Vol. 58, pp. 345–363, 1936. [56] 50. Alan M. Turing, “On Computable Numbers, with an Application to the Entscheidungsproblem,” Proceedings of the London Mathematical Society, Series 2, Vol. 42, pp. 230–265, 1936–1937. [56] 51. For more information about Turing, his life and works, see the Web pages maintained by the Turing biographer, Andrew Hodges, at http://www.turing.org.uk/turing/. [56] 52. The quotation is from Alan M. Turing, “Lecture to the London Mathematical Society,” p. 112, typescript in King’s College, Cambridge, published in Alan M. Turing’s ACE Report of 1946 and Other Papers (edited by B. E. Carpenter and R. W. Doran, Cambridge, MA: MIT Press, 1986), and in Volume 3 of The Collected Works of A. M. Turing (edited D. C. Ince, Amsterdam: North-Holland 1992). [58] 53. For a biographical sketch, see http://www.research.att.com/∼njas/doc/shannonbio.html. [58] 54. In his book The Mind’s New Science, Howard Gardner called this thesis “possibly the most important, and also the most famous, master’s thesis of the century.” [58] 55. Various sources give different dates for the air raid, but a letter in the possession of Zuse’s son, Horst Zuse, gives the 1943 date (according to an e-mail sent me on February 10, 2009, by Wolfgang Bibel, who has communicated with Horst Zuse). [59] 56. A copy of the report, plus introductory commentary, can be found at http://qss.stanford.edu/∼godfrey/. [60] 57. Alan M. Turing, “Intelligent Machinery,” National Physical Laboratory Report, 1948. Reprinted in B. Meltzer and D. Michie (eds), Machine Intelligence 5, Edinburgh: Edinburgh University Press, 1969. A facsimile of the report is available online at http://www.AlanTuring.net/intelligent machinery. [61] 58. Andrew Hodges, Turing, London: Phoenix, 1997. [61] 59. Alan M. Turing, “Computing Machinery and Intelligence,” Mind, Vol. LIX, No. 236, pp. 433–460, October 1950. (Available at http://www.abelard.org/turpap/turpap.htm.) [61] 60. See the “Home Page of the Loebner Prize in Artificial Intelligence” at http://www.loebner.net/Prizef/loebner-prize.html. [62] 61. For discussion, see the Wikipedia article at http://en.wikipedia.org/wiki/Turing test. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 69
- 70. 2 NOTES [62] 62. Joseph Weizenbaum, “ELIZA—A Computer Program for the Study of Natural Language Communication between Man and Machine,” Communications of the ACM, Vol. 9, No. 1, pp. 36–35, January 1966. Available online at http://i5.nyu.edu/∼mm64/x52.9265/january1966.html. [62] 63. Allen Newell and Herbert A. Simon, “Computer Science as Empirical Inquiry: Symbols and Search,” Communications of the ACM, Vol. 19, No. 3, pp. 113–126, March 1976. [64] 64. National Academy of Sciences, Biographical Memoirs, Vol. 71, 1997. Available online at http://www.nap.edu/catalog.php?record id=5737. [66] 70 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 71. 2.3 Part II Early Explorations: 1950s and 1960s If machines are to become intelligent, they must, at the very least, be able to do the thinking-related things that humans can do. The first steps then in the quest for artificial intelligence involved identifying some specific tasks thought to require intelligence and figuring out how to get machines to do them. Solving puzzles, playing games such as chess and checkers, proving theorems, answering simple questions, and classifying visual images were among some of the problems tackled by the early pioneers during the 1950s and early 1960s. Although most of these were laboratory-style, sometimes called “toy,” problems, some real-world problems of commercial importance, such as automatic reading of highly stylized magnetic characters on bank checks and language translation, were also being attacked. (As far as I know, Seymour Papert was the first to use the phrase “toy problem.” At a 1967 AI workshop I attended in Athens, Georgia, he distinguished among tau or “toy” problems, rho or real-world problems, and theta or “theory” problems in artificial intelligence. This distinction still serves us well today.) In this part, I’ll describe some of the first real efforts to build intelligent machines. Some of these were discussed or reported on at conferences and symposia – making these meetings important milestones in the birth of AI. I’ll also do my best to explain the underlying workings of some of these early AI programs. The rather dramatic successes during this period helped to establish a solid base for subsequent artificial intelligence research. Some researchers became intrigued (one might even say captured) by the methods they were using, devoting themselves more to improving the power and generality of their chosen techniques than to applying them to the tasks Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 71
- 72. 2 thought to require them. Moreover, because some researchers were just as interested in explaining how human brains solved problems as they were in getting machines to do so, the methods being developed were often proposed as contributions to theories about human mental processes. Thus, research in cognitive psychology and research in artificial intelligence became highly intertwined. 72 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 73. 3.1 Chapter 3 Gatherings In September 1948, an interdisciplinary conference was held at the California Institute of Technology (Caltech) in Pasadena, California, on the topics of how the nervous system controls behavior and how the brain might be compared to a computer. It was called the Hixon Symposium on Cerebral Mechanisms in Behavior. Several luminaries attended and gave papers, among them Warren McCulloch, John von Neumann, and Karl Lashley (1890–1958), a prominent psychologist. Lashley gave what some thought was the most important talk at the symposium. He faulted behaviorism for its static view of brain function and claimed that to explain human abilities for planning and language, psychologists would have to begin considering dynamic, hierarchical structures. Lashley’s talk laid out the foundations for what would become cognitive science.1 The emergence of artificial intelligence as a full-fledged field of research coincided with (and was launched by) three important meetings – one in 1955, one in 1956, and one in 1958. In 1955, a “Session on Learning Machines” was held in conjunction with the 1955 Western Joint Computer Conference in Los Angeles. In 1956 a “Summer Research Project on Artificial Intelligence” was convened at Dartmouth College. And in 1958 a symposium on the “Mechanization of Thought Processes,” was sponsored by the National Physical Laboratory in the United Kingdom. 3.1 Session on Learning Machines Four important papers were presented in Los Angeles in 1955. In his chairman’s introduction to this session, Willis Ware wrote These papers do not suggest that future learning machines should be built in the pattern of the general-purpose digital computing Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 73
- 74. 3 Gatherings device; it is rather that the digital computing system offers a convenient and highly flexible tool to probe the behavior of the models. . . . This group of papers suggests directions of improvement for future machine builders whose intent is to utilize digital computing machinery for this particular model technique. Speed of operation must be increased manyfold; simultaneous operation in many parallel modes is strongly indicated; the size of random access storage must jump several orders of magnitude; new types of input–output equipment are needed. With such advancements and the techniques discussed in these papers, there is considerable promise that systems can be built in the relatively near future which will imitate considerable portions of the activity of the brain and nervous system. Fortunately, we have made substantial progress on the items on Ware’s list of “directions for improvement.” Speed of operation has increased manyfold, parallel operation is utilized in many AI systems, random access storage has jumped several orders of magnitude, and many new types of input–output equipment are available. Perhaps even further improvements will be necessary. The session’s first paper, by Wesley Clark and Belmont Farley of MIT’s Lincoln Laboratory, described some pattern-recognition experiments on networks of neuron-like elements.2 Motivated by Hebb’s proposal that assemblies of neurons could learn and adapt by adjusting the strengths of their interconnections, experimenters had been trying various schemes for adjusting the strengths of connections within their networks, which were usually simulated on computers. Some just wanted to see what these networks might do whereas others, such as Clark and Farley, were interested in specific applications, such as pattern recognition. To the dismay of neurophysiologists, who complained about oversimplification, these networks came to be called neural networks. Clark and Farley concluded that “crude but useful generalization properties are possessed even by randomly connected nets of the type described.”3 The next pair of papers, one by Gerald P. Dinneen (1924– ) and one by Oliver Selfridge (1926–2008; Fig. 3.1), both from MIT’s Lincoln Laboratory, presented a different approach to pattern recognition. Dinneen’s paper4 described computational techniques for processing images. The images were presented to the computer as a rectangular array of intensity values corresponding to the various shades of gray in the image. Dinneen pioneered the use of filtering methods to remove random bits of noise, thicken lines, and find edges. He began his paper with the following: Over the past months in a series of after-hour and luncheon meetings, a group of us at the laboratory have speculated on problems in this area. Our feeling, pretty much unanimously, was that there is a real need to get practical, to pick a real live problem 74 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 75. 3.1 Session on Learning Machines and go after it. Selfridge’s paper5 was a companion piece to that of Dinneen. Operating on “cleaned-up” images (as might be produced by Dinneen’s program, for example), Selfridge described techniques for highlighting “features” in these images and then classifying them based on the features. For example, corners of an image known to be either a square or a triangle are highlighted, and then the number of corners is counted to determine whether the image is of a square or of a triangle. Selfridge said that “eventually, we hope to be able to recognize other kinds of features, such as curvature, juxtaposition of singular points (that is, their relative bearings and distances), and so forth.” Figure 3.1: Oliver Selfridge. (Photograph courtesy of Oliver Selfridge.) The methods pioneered by Selfridge and Dinneen are fundamental to most of the later work in enabling machines to “see.” Their work is all the more remarkable when one considers that it was done on a computer, the Lincoln Laboratory “Memory Test Computer,” that today would be regarded as extremely primitive. [The Memory Test Computer (MTC) was the first to use the ferrite core random-access memory modules developed by Jay Forrester. It was designed and built by Ken Olsen in 1953 at the Digital Equipment Corporation (DEC). The MTC was the first computer to simulate the operation of neural networks – those of Clark and Farley.] The next paper6 was about programming a computer to play chess. It was written by Allen Newell, then a researcher at the Rand Corporation in Santa Monica. Thanks to a biographical sketch of Newell written by his Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 75
- 76. 3 Gatherings colleague, Herb Simon of Carnegie Mellon University, we know something about Newell’s motivation and how he came to be interested in this problem:7 In September 1954 Allen attended a seminar at RAND in which Oliver Selfridge of Lincoln Laboratory described a running computer program that learned to recognize letters and other patterns. While listening to Selfridge characterizing his rather primitive but operative system, Allen experienced what he always referred to as his “conversion experience.” It became instantly clear to him “that intelligent adaptive systems could be built that were far more complex than anything yet done.” To the knowledge Allen already had about computers (including their symbolic capabilities), about heuristics, about information processing in organizations, about cybernetics, and proposals for chess programs was now added a concrete demonstration of the feasibility of computer simulation of complex processes. Right then he committed himself to understanding human learning and thinking by simulating it. Simon goes on to summarize Newell’s paper on chess: [It] outlined an imaginative design for a computer program to play chess in humanoid fashion, incorporating notions of goals, aspiration levels for terminating search, satisfying with “good enough” moves, multidimensional evaluation functions, the generation of subgoals to implement goals, and something like best first search. Information about the board was to be expressed symbolically in a language resembling the predicate calculus. The design was never implemented, but ideas were later borrowed from it for use in the NSS [Newell, Shaw, and Simon] chess program in 1958.8 Newell hinted that his aims extended beyond chess. In his paper he wrote “The aim of this effort, then, is to program a current computer to learn to play good chess. This is the means to understanding more about the kinds of computers, mechanisms, and programs that are necessary to handle ultracomplicated problems.” Newell’s proposed techniques can be regarded as his first attempt to produce evidence for what he and Simon later called the Physical Symbol System Hypothesis. Walter Pitts, a commentator for this session, concluded it by saying, “But, whereas Messrs. Farley, Clark, Selfridge, and Dinneen are imitating the nervous system, Mr. Newell prefers to imitate the hierarchy of final causes traditionally called the mind. It will come to the same thing in the end, no doubt. . . .” To “come to the same thing,” these two approaches, neural modeling and symbol processing, must be recognized simply as different levels 76 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 77. 3.2 The Dartmouth Summer Project of description of what goes on in the brain. Different levels are appropriate for describing different kinds of mental phenomena. I’ll have more to say about description levels later in the book. 3.2 The Dartmouth Summer Project In 1954, John McCarthy (1927– ; Fig 3.2) joined Dartmouth College in Hanover, New Hampshire, as an Assistant Professor of Mathematics. McCarthy had been developing a continuing interest in what would come to be called artificial intelligence. It was “triggered,” he says, “by attending the September 1948 Hixon Symposium on Cerebral Mechanisms in Behavior held at Caltech where I was starting graduate work in mathematics.”9 While at Dartmouth he was invited by Nathaniel Rochester (1919–2001) to spend the summer of 1955 in Rochester’s Information Research Department at IBM in Poughkeepsie, New York. Rochester had been the designer of the IBM 701 computer and had also participated in research on neural networks.10 At IBM that summer, McCarthy and Rochester persuaded Claude Shannon and Marvin Minsky (1927– ; Fig. 3.2), then a Harvard junior fellow in mathematics and neurology, to join them in proposing a workshop to be held at Dartmouth during the following summer. Shannon, whom I have previously mentioned, was a mathematician at Bell Telephone Laboratories and already famous for his work on switching theory and statistical information theory. McCarthy took the lead in writing the proposal and in organizing what was to be called a “Summer Research Project on Artificial Intelligence.” The proposal was submitted to the Rockefeller Foundation in August 1955. Extracts from the proposal read as follows:11 We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer. . . . For the present purpose the artificial intelligence problem is taken to be that of making a machine behave in ways that would be called intelligent if a human were so behaving. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 77
- 78. 3 Gatherings Figure 3.2: John McCarthy (left) and Marvin Minsky (right). (McCarthy photo- graph courtesy of John McCarthy. Minsky photograph courtesy MIT Museum.) The Rockefeller Foundation did provide funding for the event, which took place during six weeks of the summer of 1956. It turned out, however, to be more of a rolling six-week workshop than a summer “study.” Among the people attending the workshop that summer, in addition to McCarthy, Minsky, Rochester, and Shannon were Arthur Samuel (1901–1990), an engineer at the IBM corporation who had already written a program to play checkers, Oliver Selfridge, Ray Solomonoff of MIT, who was interested in automating induction, Allen Newell, and Herbert Simon. Newell and Simon (together with another Rand scientist, Cliff Shaw) had produced a program for proving theorems in symbolic logic. Another attending IBM scientist was Alex Bernstein, who was working on a chess-playing program. McCarthy has given a couple of reasons for using the term “artificial intelligence.” The first was to distinguish the subject matter proposed for the Dartmouth workshop from that of a prior volume of solicited papers, titled Automata Studies, co-edited by McCarthy and Shannon, which (to McCarthy’s disappointment) largely concerned the esoteric and rather narrow mathematical subject called “automata theory.” The second, according to McCarthy, was “to escape association with ‘cybernetics.’ Its concentration on analog feedback seemed misguided, and I wished to avoid having either to accept Norbert Wiener as a guru or having to argue with him.”12 78 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 79. 3.2 The Dartmouth Summer Project There was (and still is) controversy surrounding the name. According to Pamela McCorduck’s excellent history of the early days of artificial intelligence, Art Samuel remarked, “The word artificial makes you think there’s something kind of phony about this, or else it sounds like it’s all artificial and there’s nothing real about this work at all.”13 McCorduck goes on to say that “[n]either Newell or Simon liked the phrase, and called their own work complex information processing for years thereafter.” But most of the people who signed on to do work in this new field (including myself) used the name “artificial intelligence,” and that is what the field is called today. (Later, Newell became reconciled to the name. In commenting about the content of the field, he concluded, “So cherish the name artificial intelligence. It is a good name. Like all names of scientific fields, it will grow to become exactly what its field comes to mean.”)14 The approaches and motivations of the people at the workshop differed. Rochester came to the conference with a background in networks of neuron-like elements. Newell and Simon had been pursuing (indeed had helped originate) the symbol-processing approach. Among the topics Shannon wanted to think about (according to the proposal) was the “application of information theory concepts to computing machines and brain models.” (After the workshop, however, Shannon turned his attention away from artificial intelligence.) McCarthy wrote that he was interested in constructing “an artificial language which a computer can be programmed to use on problems requiring conjecture and self-reference. It should correspond to English in the sense that short English statements about the given subject matter should have short correspondents in the language and so should short arguments or conjectural arguments. I hope to try to formulate a language having these properties . . . ” Although McCarthy later said that his ideas on this topic were still too “ill formed” for presentation at the conference, it was not long before he made specific proposals for using a logical language and its inference mechanisms for representing and reasoning about knowledge. Although Minsky’s Ph.D. dissertation15 and some of his subsequent work concentrated on neural nets, around the time of the Dartmouth workshop he was beginning to change direction. Now, he wrote, he wanted to consider a machine that “would tend to build up within itself an abstract model of the environment in which it is placed. If it were given a problem, it could first explore solutions within the internal abstract model of the environment and then attempt external experiments.” At the workshop, Minsky continued work on a draft that was later to be published as a foundational paper, “Steps Toward Artificial Intelligence.”16 One of the most important technical contributions of the 1956 meeting was work presented by Newell and Simon on their program, the “Logic Theorist (LT),” for proving theorems in symbolic logic. LT was concrete evidence that processing “symbol structures” and the use of what Newell and Simon called “heuristics” were fundamental to intelligent problem solving. I’ll Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 79
- 80. 3 Gatherings describe some of these ideas in more detail in a subsequent chapter. Newell and Simon had been working on ideas for LT for some months and became convinced in late 1955 that they could be embodied in a working program. According to Edward Feigenbaum (1936– ), who was taking a course from Herb Simon at Carnegie in early 1956, “It was just after Christmas vacation – January 1956 – when Herb Simon came into the classroom and said, ‘Over Christmas Allen Newell and I invented a thinking machine.’”17 What was soon to be programmed as LT was the “thinking machine” Simon was talking about. He called it such, no doubt, because he thought it used some of the same methods for solving problems that humans use. Simon later wrote18 “On Thursday, Dec. 15. . . I succeeded in simulating by hand the first proof. . . I have always celebrated Dec. 15, 1955, as the birthday of heuristic problem solving by computer.” According to Simon’s autobiography Models of My Life,19 LT began by hand simulation, using his children as the computing elements, while writing on and holding up note cards as the registers that contained the state variables of the program.20 Another topic discussed at Dartmouth was the problem of proving theorems in geometry. (Perhaps some readers will recall their struggles with geometry proofs in high school.) Minsky had already been thinking about a program to prove geometry theorems. McCorduck quotes him as saying the following:21 [P]robably the important event in my own development – and the explanation of my perhaps surprisingly casual acceptance of the Newell–Shaw–Simon work – was that I had sketched out the heuristic search procedure for [a] geometry machine and then been able to hand-simulate it on paper in the course of an hour or so. Under my hand the new proof of the isosceles-triangle theorem came to life, a proof that was new and elegant to the participants – later, we found that proof was well-known. . . In July 2006, another conference was held at Dartmouth celebrating the fiftieth anniversary of the original conference. (See Fig. 3.3.) Several of the founders and other prominent AI researchers attended and surveyed what had been achieved since 1956. McCarthy reminisced that the “main reason the 1956 Dartmouth workshop did not live up to my expectations is that AI is harder than we thought.” In any case, the 1956 workshop is considered to be the official beginning of serious work in artificial intelligence, and Minsky, McCarthy, Newell, and Simon came to be regarded as the “fathers” of AI. A plaque was dedicated and installed at the Baker Library at Dartmouth commemorating the beginning of artificial intelligence as a scientific discipline. 80 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 81. 3.3 Mechanization of Thought Processes Figure 3.3: Some of AI’s founders at the July 2006 Dartmouth fiftieth anniver- sary meeting. From the left are Trenchard More, John McCarthy, Marvin Min- sky, Oliver Selfridge, and Ray Solomonoff. (Photograph courtesy of photogra- pher Joe Mehling and the Dartmouth College Artificial Intelligence Conference: The Next Fifty Years.) 3.3 Mechanization of Thought Processes In November 1958, a symposium on the “Mechanisation of Thought Processes” was held at the National Physical Laboratory in Teddington, Middlesex, England. According to the preface of the conference proceedings, the symposium was held “to bring together scientists studying artificial thinking, character and pattern recognition, learning, mechanical language translation, biology, automatic programming, industrial planning and clerical mechanization.” Among the people who presented papers at this symposium were many whom I have already mentioned in this story. They include Minsky (by then a staff member at Lincoln Laboratory and on his way to becoming an assistant professor of Mathematics at MIT), McCarthy (by then an assistant professor of Communication Sciences at MIT), Ashby, Selfridge, and McCulloch. (John Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 81
- 82. 3 Gatherings Backus, one of the developers of the computer programming language FORTRAN, and Grace Murray Hopper, a pioneer in “automatic programming,” also gave papers.) The proceedings of this conference22 contains some papers that became quite influential in the history of artificial intelligence. Among these, I’ll mention ones by Minsky, McCarthy, and Selfridge. Minsky’s paper, “Some Methods of Artificial Intelligence and Heuristic Programming,” was the latest version of a piece he had been working on since just before the Dartmouth workshop. The paper described various methods that were (and could be) used in heuristic programming. It also covered methods for pattern recognition, learning, and planning. The final version, which was soon to be published as “Steps Toward Artificial Intelligence,” was to become required reading for new recruits to the field (including me). I have already mentioned McCarthy’s hope to develop an artificial language for AI. He summarized his conference paper, “Programs with Common Sense,” as follows: This paper will discuss programs to manipulate in a suitable formal language (most likely a part of the predicate calculus) common instrumental statements. The basic program will draw immediate conclusions from a list of premises. These conclusions will be either declarative or imperative sentences. When an imperative sentence is deduced, the program takes a corresponding action. In his paper, McCarthy suggested that facts needed by an AI program, which he called the “advice taker,” might be represented as expressions in a mathematical (and computer-friendly) language called “first-order logic.” For example, the facts “I am at my desk” and “My desk is at home” would be represented as the expressions at(I, desk) and at(desk, home). These, together with similarly represented information about how to achieve a change in location (by walking and driving for example), could then be used by the proposed (but not yet programmed) advice taker to figure out how to achieve some goal, such as being at the airport. The advice taker’s reasoning process would produce imperative logical expressions involving walking to the car and driving to the airport. Representing facts in a logical language has several advantages. As McCarthy later put it,23 Expressing information in declarative sentences is far more modular than expressing it in segments of computer program or in tables. Sentences can be true in much wider contexts than specific programs can be useful. The supplier of a fact does not have to understand much about how the receiver functions, or how or whether the receiver will use it. The same fact can be used for 82 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 83. 3.3 Mechanization of Thought Processes many purposes, because the logical consequences of collections of facts can be available. McCarthy later expanded on these ideas in a companion memorandum.24 As I’ll mention later, some of McCarthy’s advice-taker proposals were finally implemented by a Stanford graduate student, C. Cordell Green. I have already mentioned the 1955 pattern-recognition work of Oliver Selfridge. At the 1958 Teddington Symposium, Selfridge presented a paper on a new model for pattern recognition (and possibly for other cognitive tasks also).25 He called it “Pandemonium,” meaning the place of all the demons. His model is especially interesting because its components, which Selfridge called “demons,” can either be instantiated as performing lower level nerve-cell-type functions or higher level cognitive functions (of the symbol-processing variety). Thus, Pandemonium can take the form of a neural network, a hierarchically organized set of symbol processors – all working in parallel, or some combination of these forms. If the latter, the model is a provocative proposal for joining these two disparate approaches to AI. In the introduction to his paper, Selfridge emphasized the importance of computations performed in parallel: The basic motif behind our model is the notion of parallel processing. This is suggested on two grounds: first, it is often easier to handle data in a parallel manner, and, indeed, it is usually the more “natural” manner to handle it in; and, secondly, it is easier to modify an assembly of quasi-independent modules than a machine all of whose parts interact immediately and in a complex way. Selfridge made several suggestions about how Pandemonium could learn. It’s worth describing some of these because they foreshadow later work in machine learning. But first I must say a bit more about the structure of Pandemonium. Pandemonium’s structure is something like that of a business organization chart. At the bottom level are workers, whom Selfridge called the “data demons.” These are computational processes that “look at” the input data, say an image of a printed letter or number. Each demon looks for something specific in the image, perhaps a horizontal bar; another might look for a vertical bar; another for an arc of a circle; and so on. Each demon “shouts” its findings to a set of demons higher in the organization. (Think of these higher level demons as middle-level managers.) The loudness of a demon’s shout depends on how certain it is that it is seeing what it is looking for. Of course, Selfridge is speaking metaphorically when he uses terms such as “looking for” and “shouting.” Suffice it to say that it is not too difficult to program computers to “look for” certain features in an image. (Selfridge had already shown how that could be done in his 1955 paper that I mentioned earlier.) And a “shout” is really the strength of the output of a computational process. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 83
- 84. 3 Gatherings Each of the next level of demons specializes in listening for a particular combination of shouts from the data demons. For example, one of the demons at this level might be tuned to listen for shouts from data demon 3, data demon 11, and data demon 22. If it finds that these particular demons are shouting loudly, it responds with a shout of its own to the demons one level up in the hierarchy, and so on. Just below the top level of the organization are what Selfridge called the “cognitive demons.” As at the other levels, these listen for particular combinations of shouts from the demons at the level below, and they respond with shouts of their own to a final “decision demon” at the top – the overall boss. Depending on what it hears from its “staff,” the decision demon finally announces what it thinks is the identity of the image – perhaps the letter “A” or the letter “R” or whatever. Actual demon design depends on what task Pandemonium is supposed to be doing. But even without specifying what each demon was to do, Selfridge made very interesting proposals about how Pandemonium could learn to perform better at whatever it was supposed to be doing. One of his proposals involved equipping each demon with what amounted to a “megaphone” through which it delivered its shout. The volume level of the megaphone could be adjusted. (Selfridge’s Pandemonium is just a bit more complicated than the version I am describing. His version has each demon using different channels for communicating with each of the different demons above it. The volume of the shout going up each channel is individually adjusted by the learning mechanism.) The demons were not allowed to set their own volume levels, however. All volume levels were to be set through an outside learning process attempting to improve the performance of the whole assembly. Imagine that the volume levels are initially set either at random or at whatever a designer thinks would be appropriate. The device is then tested on some sample of input data and its performance score is noted. Say, it gets a score of 81%. Then, small adjustments are made to the volume levels in all possible ways until a set of adjustments is found that improves the score the most, say to 83%. This particular set of small adjustments is then made and the process is repeated over and over (possibly on additional data) until no further improvement can be made. (Because there might be a lot of megaphones in the organization, it might seem impractical to make adjustments in all possible ways and to test each of these ways to find its score. The process might indeed take some time, but computers are fast – even more so today. Later in the book, I’ll show how one can calculate, rather than find by experiment, the best adjustments to make in neural networks organized like Pandemonium.) If we think of the score as the height of some landscape and the adjustments as movements over the landscape, the process can be likened to climbing a hill by always taking steps in the direction of steepest ascent. Gradient ascent (or hill-climbing methods, as they are sometimes called) are 84 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 85. 3.3 NOTES well known in mathematics. Selfridge had this to say about some of the pitfalls of their use: This may be described as one of the problems of training, namely, to encourage the machine or organism to get enough on the foot-hills so that small changes. . . will produce noticeable improvement in his altitude or score. One can describe learning situations where most of the difficulty of the task lies in finding any way of improving one’s score, such as learning to ride a unicycle, where it takes longer to stay on for a second than it does to improve that one second to a minute; and others where it is easy to do a little well and very hard to do very well, such as learning to play chess. It’s also true that often the main peak is a plateau rather than an isolated spike. Selfridge described another method for learning in Pandemonium. This method might be likened to replacing managers in an organization who do not perform well. As Selfridge puts it, At the conception of our demoniac assembly we collected somewhat arbitrarily a large number of subdemons which we guessed would be useful. . . but we have no assurance at all that the particular subdemons we selected are good ones. Subdemon selection generates new subdemons for trial and eliminates inefficient ones, that is, ones that do not much help improve the score. The demon selection process begins after the volume-adjusting learning mechanism has run for a while with no further improvements in the score. Then the “worth” of each demon is evaluated by using, as Selfridge suggests, a method based on the learned volume levels of their shouting. Demons having high volume levels have a large effect on the final score, and so they can be thought to have high worth. First, the demons with low volume levels are eliminated entirely. (That step can’t hurt the score very much.) Next, some of the demons undergo random “mutations” and are put back in service. Next, some pairs of worthy demons are selected and, as Selfridge says, “conjugated” into offspring demons. The precise method Selfridge proposed for conjugation need not concern us here, but the spirit of the process is to produce offspring that share, one hopes, useful properties of the parents. The offspring are then put into service. Now the whole process of adjusting volume levels of the surviving and “evolved” demons can begin again to see whether the score of the new assembly can be further improved. Notes Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 85
- 86. 3 NOTES 1. The proceedings of the symposium were published in L. A. Jeffries (ed.), Cerebral Mechanisms in Behavior: The Hixon Symposium, New York: Wiley, 1951. An excellent review of Lashley’s points are contained in Chapter 2 of The Mind’s New Science: A History of the Cognitive Revolution, by Howard E Gardner, New York: Basic Books, 1985. [73] 2. W. A. Clark and B. G. Farley, “Generalization of Pattern Recognition in a Self-Organizing System,” Proceedings of the 1955 Western Joint Computer Conference, Institute of Radio Engineers, New York, pp. 86–91, 1955. Clark and Farley’s experiments continued some work they had reported on earlier in B. G. Farley and W. A. Clark, “Simulation of Self-Organizing Systems by Digital Computer, IRE Transactions on Information Theory, Vol. 4, pp. 76–84, 1954. (In 1962 Clark built the first personal computer, the LINC.) [74] 3. Alan Wilkes and Nicholas Wade credit Scottish psychologist Alexander Bain (1818–1903) with the invention of the first neural network, which Bain described in his 1873 book Mind and Body: The Theories of Their Relation.” (See Alan L. Wilkes and Nicholas J. Wade, “Bain on Neural Networks,” Brain and Cognition, Vol. 33, pp. 295–305, 1997.) [74] 4. Gerald P. Dinneen, “Programming Pattern Recognition,” Proceedings of the 1955 Western Joint Computer Conference, Institute of Radio Engineers, New York, pp. 94–100, 1955. [74] 5. Oliver Selfridge, “Pattern Recognition and Modern Computers,” Proceedings of the 1955 Western Joint Computer Conference, Institute of Radio Engineers, New York, pp. 91–93, 1955. [75] 6. Allen Newell, “The Chess Machine: An Example of Dealing with a Complex Task by Adaptation,” Proceedings of the 1955 Western Joint Computer Conference, Institute of Radio Engineers, New York, pp. 101–108, 1955. (Also issued as RAND Technical Report P-620.) [75] 7. National Academy of Sciences, Biographical Memoirs, Vol. 71, 1997. Available online at http://www.nap.edu/catalog.php?record id=5737. [76] 8. Allen Newell, J. C. Shaw, and Herbert A. Simon, “Chess-Playing Programs and the Problem of Complexity,” IBM Journal of Research and Development, Vol. 2, pp. 320–335, 1958. The paper is available online at http://domino.watson.ibm.com/tchjr/journalindex. nsf/0/237cfeded3be103585256bfa00683d4d?OpenDocument. [76] 9. From John McCarthy’s informal comments at the 2006 Dartmouth celebration. [77] 10. Nathan Rochester et al., “Tests on a Cell Assembly Theory of the Action of the Brain Using a Large Digital Computer,” IRE Transaction of Information Theory, Vol. IT-2, pp. 80-93, 1956. [77] 11. From http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html. Portions of the proposal have been reprinted in John McCarthy, Marvin L. Minsky, Nathaniel Rochester, and Claude E. Shannon, “A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence,” AI Magazine, Vol. 27, No. 4, p. 12, Winter 2006. [77] 12. From http://www-formal.stanford.edu/jmc/reviews/bloomfield/bloomfield.html. [78] 13. Pamela McCorduck, Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence, p. 97, San Francisco: W. H. Freeman and Co., 1979. [79] 14. See Allen Newell, “The First AAAI President’s Message,” AI Magazine, Vol. 26, No. 4, pp. 24–29, Winter 2005. [79] 15. M. L. Minsky, Theory of Neural-Analog Reinforcement Systems and Its Application to the Brain-Model Problem, Ph.D. thesis, Princeton University, 1954. [79] 16. Marvin L. Minsky, “Steps Toward Artificial Intelligence,” Proceedings of the IRE, Vol. 49, No. 1, pp. 8–30, January 1961. Also appears in Edward A. Feigenbaum, and Julian 86 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 87. 3.3 NOTES Feldman (eds.), Computers and Thought, New York: McGraw Hill, 1963. (Available online at http://web.media.mit.edu/∼minsky/papers/steps.html.) [79] 17. Pamela McCorduck, op. cit., p. 116. [80] 18. Herbert A. Simon, Models of My Life, Cambridge, MA: MIT Press, 1996. The quote is from http://www.post-gazette.com/pg/06002/631149.stm. [80] 19. Ibid. [80] 20. http://www.post-gazette.com/downloads/20060102simon notes.pdf contains sketches of Simon’s simulation of an LT proof. [80] 21. Pamela McCorduck, op. cit., p. 106. [80] 22. D. V. Blake and A. M. Uttley (eds.), Proceedings of the Symposium on Mechanisation of Thought Processes, Vols. 1 and 2, London: Her Majesty’s Stationary Office, 1959. [82] 23. John McCarthy, “Artificial Intelligence, Logic and Formalizing Common Sense,” in Philosophical Logic and Artificial Intelligence, Richmond Thomason (ed.), Dordrecht: Kluwer Academic, 1989. [82] 24. J. McCarthy, “Situations, Actions and Causal Laws, Stanford Artificial Intelligence Project,” Memo 2, 1963. The two pieces are reprinted together in M. Minsky (ed.), Semantic Information Processing, pp. 410–417, Cambridge, MA: MIT Press, 1968. Related topics are explored in J. McCarthy and Patrick Hayes, “Some Philosophical Ideas From the Standpoint of Artificial Intelligence,” MI-4, 1969. [83] 25. Oliver G. Selfridge, “Pandemonium: A Paradigm for Learning,” in D. V. Blake and A. M. Uttley (eds.), Proceedings of the Symposium on Mechanisation of Thought Processes, pp, 511–529, London: Her Majesty’s Stationary Office, 1959. [83] Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 87
- 88. 3 NOTES 88 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 89. 4.1 Chapter 4 Pattern Recognition Most of the attendees of the Dartmouth summer project were interested in mimicking the higher levels of human thought. Their work benefitted from a certain amount of introspection about how humans solve problems. Yet, many of our mental abilities are beyond our power of introspection. We don’t know how we recognize speech sounds, read cursive script, distinguish a cup from a plate, or identify faces. We just do these things automatically without thinking about them. Lacking clues from introspection, early researchers interested in automating some of our perceptual abilities based their work instead on intuitive ideas about how to proceed, on networks of simple models of neurons, and on statistical techniques. Later, workers gained additional insights from neurophysiological studies of animal vision. In this chapter, I’ll describe work during the 1950s and 1960s on what is called “pattern recognition.” This phrase refers to the process of analyzing an input image, a segment of speech, an electronic signal, or any other sample of data and classifying it into one of several categories. For character recognition, for example, the categories would correspond to the several dozen or so alphanumeric characters. Most of the pattern-recognition work in this period dealt with two-dimensional material such as printed pages or photographs. It was already possible to scan images to convert them into arrays of numbers (later called “pixels”), which could then be processed by computer programs such as those of Dinneen and Selfridge. Russell Kirsch and colleagues at the National Bureau of Standards (now the National Institute for Standards and Technology) were also among the early pioneers in image processing. In 1957 Kirsch built and used a drum scanner to scan a photograph of his three-month-old son, Walden. Said to be the first scanned photograph, it measured 176 pixels on a side and is depicted in Fig. 4.1.1 Using his scanner, he and colleagues experimented with picture-procesing programs running on their SEAC (Standards Eastern Automatic Computer) computer.2 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 89
- 90. 4 Pattern Recognition Figure 4.1: An early scanned photograph. (Photograph used with permission of NIST.) 4.1 Character Recognition Early efforts at the perception of visual images concentrated on recognizing alphanumeric characters on documents. This field came to be known as “optical character recognition.” A symposium devoted to reporting on progress on this topic was held in Washington, DC, in January 1962.3 In summary, devices existed at that time for reasonably accurate recognition of fixed-font (typewritten or printed) characters on paper. Perhaps the state of things then was best expressed by one of the participants of the symposium, J. Rabinow of Rabinow Engineering, who said “We think, in our company, that we can read anything that is printed, and we can even read some things that are written. The only catch is, ‘how many bucks do you have to spend?’”4 A notable success during the 1950s was the magnetic ink character recognition (MICR) system developed by researchers at SRI International (then called the Stanford Research Institute) for reading stylized magnetic ink characters at the bottom of checks. (See Fig. 4.2.) MICR was part of SRI’s ERMA (Electronic Recording Method of Accounting) system for automating check processing and checking account management and posting. According to an SRI Web site, “In April 1956, the Bank of America announced that General Electric Corporation had been selected to manufacture production models. . . . In 1959, General Electric delivered the first 32 ERMA computing systems to the Bank of America. ERMA served as the Bank’s accounting computer and check handling system until 1970.”5 90 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 91. 4.1 Character Recognition Figure 4.2: The MICR font set. Most of the recognition methods at that time depended on matching a character (after it was isolated on the page and converted to an array of 0’s and 1’s) against prototypical versions of the character called “templates” (also stored as arrays in the computer). If a character matched the template for an “A,” say, sufficiently better than it matched any other templates, the input was declared to be an “A.” Recognition accuracy degraded if the input characters were not presented in standard orientation, were not of the same font as the template, or had imperfections. The 1955 papers by Selfridge and Dinneen (which I have already mentioned on p. 74) proposed some ideas for moving beyond template matching. A 1960 paper by Oliver Selfridge and Ulrich Neisser carried this work further.6 That paper is important because it was a successful, early attempt to use image processing, feature extraction, and learned probability values in hand-printed character recognition. The characters were scanned and represented on a 32 × 32 “retina” or array of 0’s and 1’s. They were then processed by various refining operations (similar to those I mentioned in connection with the 1955 Dinneen paper) for removing random bits of noise, filling gaps, thickening lines, and enhancing edges. The “cleaned-up” images were then inspected for the occurrence of “features” (similar to the features I mentioned in connection with the 1955 Selfridge paper.) In all, 28 features were used – features such as the maximum number of times a horizontal line intersected the image, the relative lengths of different edges, and whether or not the image had a “concavity facing south.” Recalling Selfridge’s Pandemonium system, we can think of the feature-detection process as being performed by “demons.” At one level higher in the hierarchy than the feature demons were the “recognition demons” – one for each letter. (The version of this system tested by Worthie Doyle of Lincoln Laboratory was designed to recognize ten different hand-printed characters, namely, A, E, I, L, M, N, O, R, S, and T.) Each recognition demon received inputs from each of the feature-detecting demons. But first, the inputs to each recognition demon were multiplied by a weight that took into account the importance of the contribution of the corresponding feature to the decision. For example, if feature 17 were more important than feature 22 in deciding that the input character was an “A,” then the input to the “A” recognizer Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 91
- 92. 4 Pattern Recognition from feature 17 would be weighted more heavily than would be the input from feature 22. After each recognition demon added up the total of its weighted inputs, a final “decision demon” decided in favor of that character having the largest sum. The values of the weights were determined by a learning process during which 330 “training” images were analyzed. Counts were tabulated for how many times each feature was detected for each different letter in the training set. These statistical data were used to make estimates of the probabilities that a given feature would be detected for each of the letters. These probability estimates were then used to weight the features summed by the recognizing demons. After training, the system was tested on samples of hand-printed characters that it had not yet seen. According to Selfridge and Neisser, “This program makes only about 10 percent fewer correct identifications than human readers make – a respectable performance, to be sure.” 4.2 Neural Networks 4.2.1 Perceptrons In 1957, Frank Rosenblatt (1928–1969; Fig. 4.3), a psychologist at the Cornell Aeronautical Laboratory in Buffalo, New York, began work on neural networks under a project called PARA (Perceiving and Recognizing Automaton). He was motivated by the earlier work of McCulloch and Pitts and of Hebb and was interested in these networks, which he called perceptrons, as potential models of human learning, cognition, and memory.7 Continuing during the early 1960s as a professor at Cornell University in Ithaca, New York, he experimented with a number of different kinds of perceptrons. His work, more than that of Clark and Farley and of the other neural network pioneers, was responsible for initiating one of the principal alternatives to symbol-processing methods in AI, namely, neural networks. Rosenblatt’s perceptrons consisted of McCulloch–Pitts-style neural elements, like the one shown in Fig. 4.4. Each element had inputs (coming in from the left in the figure), “weights” (shown by bulges on the input lines), and one output (going out to the right). The inputs had values of either 1 or 0, and each input was multiplied by its associated weight value. The neural element computed the sum of these weighted values. So, for example, if all of the inputs to the neural element in Fig. 4.4 were equal to 1, the sum would be 13. If the sum were greater than (or just equal to) a “threshold value,” say 7, associated with the element, then the output of the neural element would be 1, which it would be in this example. Otherwise the output would be 0. A perceptron consists of a network of these neural elements, in which the 92 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 93. 4.2 Neural Networks Figure 4.3: Frank Rosenblatt (left) working (with Charles Wrightman) on a prototype A-unit. (Courtesy of the Division of Rare and Manuscript Collections, Cornell University Library.) Figure 4.4: Rosenblatt’s neural element with weights. outputs of one element are inputs to others. (There is an analogy here with Selfridge’s Pandemonium in which mid-level demons receive “shouts” from lower level demons. The weights on a neural element’s input lines can be thought of as analogous to the strength-enhancing or strength-diminishing “volume controls” in Pandemonium.) A sample perceptron is illustrated in Fig. 4.5. [Rosenblatt drew his perceptron diagrams in a horizontal format (the electrical engineering style), with inputs to the left and output to the right. Here I use the vertical style generally preferred by computer scientists for Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 93
- 94. 4 Pattern Recognition hierarchies, with the lowest level at the bottom and the highest at the top. To simplify the diagram, weight bulges are not shown.] Although the perceptron illustrated, with only one output unit, is capable of only two different outputs (1 or 0), multiple outputs (sets of 1’s and 0’s) could be achieved by arranging for several output units. Figure 4.5: A perceptron. The input layer, shown at the bottom of Fig. 4.5, was typically a rectangular array of 1’s and 0’s corresponding to cells called “pixels” of a black-and-white image. One of the applications Rosenblatt was interested in was, like Selfridge, character recognition. I’ll use some simple algebra and geometry to show how the neural elements in perceptron networks can be “trained” to produce desired outputs. 94 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 95. 4.2 Neural Networks Let’s consider, for example, a single neural element whose inputs are the values x1, x2, and x3 and whose associated weight values are w1, w2, and w3. When the sum computed by this element is exactly equal to its threshold value, say t, we have the equation w1x1 + w2x2 + w3x3 = t. In algebra, such an equation is called a “linear equation.” It defines a linear boundary, that is, a plane, in a three-dimensional space. The plane separates those input values that would cause the neural element to have an output of 1 from those that would cause it to have an output of 0. I show a typical planar boundary in Fig. 4.6. Figure 4.6: A separating plane in a three-dimensional space. An input to the neural element can be depicted as a point (that is, a vector) in this three-dimensional space. Its coordinates are the values of x1, x2, and x3, each of which can be either 1 or 0. The figure shows six such points, three of them (the small circles, say) causing the element to have an output of 1 and three (the small squares, say) causing it to have an output of 0. Changing the value of the threshold causes the plane to move sideways in a Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 95
- 96. 4 Pattern Recognition direction parallel to itself. Changing the values of the weights causes the plane to rotate. Thus, by changing the weight values, points that used to be on one side of the plane might end up on the other side. “Training” takes place by performing such changes. I’ll have more to say about training procedures presently. In dimensions higher than three (which is usually the case), a linear boundary is called a “hyperplane.” Although it is not possible to visualize what is going on in spaces of high dimensions, mathematicians still speak of input points in these spaces and rotations and movements of hyperplanes in response to changes in the values of weights and thresholds. Rosenblatt defined several types of perceptrons. He called the one shown in the diagram a “series-coupled, four-layer perceptron.” (Rosenblatt counted the inputs as the first layer.) It was termed “series-coupled” because the output of each neural element fed forward to neural elements in a subsequent layer. In more recent terminology, the phrase “feed-forward” is used instead of “series-coupled.” In contrast, a “cross-coupled” perceptron could have the outputs of neural elements in one layer be inputs to neural elements in the same layer. A “back-coupled” perceptron could have the outputs of neural elements in one layer be inputs to neural elements in lower numbered layers. Rosenblatt thought of his perceptrons as being models of the wiring of parts of the brain. For this reason, he called the neural elements in all layers but the output layer “association units” (“A-units”) because he intended them to model associations performed by networks of neurons in the brain. Of particular interest in Rosenblatt’s research was what he called an “alpha-perceptron.” It consisted of a three-layer, feed-forward network with an input layer, an association layer, and one or more output units. In most of his experiments, the inputs had values of 0 or 1, corresponding to black or white pixels in a visual image presented on what he called a “retina.” Each A-unit received inputs (which were not multiplied by weight values) from some randomly selected subset of the pixels and sent its output, through sets of adjustable weights, to the final output units, whose binary values could be interpreted as a code for the category of the input image. Various “training procedures” were tried for adjusting the weights of the output units of an alpha-perceptron. In the most successful of these (for pattern-recognition purposes), the weights leading in to the output units were adjusted only when those units made an error in classifying an input. The adjustments were such as to force the output to make the correct classification for that particular input. This technique, which soon became a standard, was called the “error-correction procedure.” Rosenblatt used it successfully in a number of experiments for training perceptrons to classify visual inputs, such as alphanumeric characters, or acoustic inputs, such as speech sounds. Professor H. David Block, a Cornell mathematician working with Rosenblatt, was able to prove that the error-correction procedure was guaranteed to find a 96 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 97. 4.2 Neural Networks hyperplane that perfectly separated a set of training inputs when such a hyperplane existed.8 (Other mathematicians, such as Albert B. Novikoff at SRI, later developed more elegant proofs.9 I give a version of this proof in my book Learning Machines.10 ) Although some feasibility and design work was done using computer simulations, Rosenblatt preferred building hardware versions of his perceptrons. (Simulations were slow on early computers, thus explaining the interest in building special-purpose perceptron hardware.) The MARK I was an alpha-perceptron built at the Cornell Aeronautical Laboratory under the sponsorship of the Information Systems Branch of the Office of Naval Research and the Rome Air Development Center. It was first publicly demonstrated on 23 June 1960. The MARK I used volume controls (called “potentiometers” by electrical engineers) for weights. These had small motors attached to them for making adjustments to increase or decrease the weight values. In 1959, Frank Rosenblatt moved his perceptron work from the Cornell Aeronautical Laboratory in Buffalo, New York, to Cornell University, where he became a professor of psychology. Together with Block and several students, Rosenblatt continued experimental and theoretical work on perceptrons. His book Principles of Neurodynamics provides a detailed treatment of his theoretical ideas and experimental results.11 Rosenblatt’s last system, called Tobermory, was built as a speech-recognition device.12 [Tobermory was the name of a cat that learned to speak in The Chronicles of Clovis, a group of short stories by Saki (H. H. Munro).] Several Ph.D. students, including George Nagy, Carl Kessler, R. D. Joseph, and others, completed perceptron projects under Rosenblatt at Cornell. In his last years at Cornell, Rosenblatt moved on to study chemical memory transfer in flatworms and other animals – a topic quite removed from his perceptron work. Tragically, Rosenblatt perished in a sailing accident in Chesapeake Bay in 1969. Around the same time as Rosenblatt’s alpha-perceptron, Woodrow W. (Woody) Bledsoe (1921–1995) and Iben Browning (1918–1991), two mathematicians at Sandia Laboratories in Albuquerque, New Mexico, were also pursuing research on character recognition that used random samplings of input images. They experimented with a system that projected images of alphanumeric characters on a 10 × 15 mosaic of photocells and sampled the states of 75 randomly chosen pairs of photocells. Pointing out that the idea could be extended to sampling larger groups of pixels, say N of them, they called their method the “N-tuple” method. They used the results of this sampling to make a decision about the category of an input letter.13 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 97
- 98. 4 Pattern Recognition 4.2.2 ADALINES and MADALINES Independently of Rosenblatt, a group headed by Stanford Electrical Engineering Professor Bernard Widrow (1929– ) was also working on neural-network systems during the late 1950s and early 1960s. Widrow had recently joined Stanford after completing a Ph.D. in control theory at MIT. He wanted to use neural-net systems for what he called “adaptive control.” One of the devices Widrow built was called an “ADALINE” (for adaptive linear network). It was a single neural element whose adjustable weights were implemented by switchable (thus adjustable) circuits of resistors. Widrow and one of his students, Marcian E. “Ted” Hoff Jr. (who later invented the first microprocessor at Intel), developed an adjustable weight they called a “memistor.” It consisted of a graphite rod on which a layer of copper could be plated and unplated – thus varying its electrical resistance. Widrow and Hoff developed a training procedure for their ADALINE neural element that came to be called the Widrow–Hoff least-mean-squares adaptive algorithm. Most of Widrow’s experimental work was done using simulations on an IBM1620 computer. Their most complex network design was called a “MADALINE” (for many ADALINEs). A training procedure was developed for it by Stanford Ph.D. student William Ridgway.14 4.2.3 The MINOS Systems at SRI Rosenblatt’s success with perceptrons on pattern-recognition problems led to a flurry of research efforts by others to duplicate and extend his results. During the 1960s, perhaps the most significant pattern-recognition work using neural networks was done at the Stanford Research Institute in Menlo Park, California. There, Charles A. Rosen (1917–2002) headed a laboratory that was attempting to etch microscopic vacuum tubes onto a solid-state substrate. Rosen speculated that circuits containing these tubes might ultimately be “wired-up” to perform useful tasks using some of the training procedures being explored by Frank Rosenblatt. SRI employed Rosenblatt as a consultant to help in the design of an exploratory neural network. When I interviewed for a position at SRI in 1960, a team in Rosen’s lab, under the leadership of Alfred E. (Ted) Brain (1923–2004), had just about completed the construction of a small neural network called MINOS (Fig. 4.7). (In Greek mythology, Minos was a king of Crete and the son of Zeus and Europa. After his death, Minos was one of the three judges in the underworld.) Brain felt that computer simulations of neural networks were too slow for practical applications, thus leading to his decision to build rather than to program. (The IBM 1620 computer being used at the same time by Widrow’s group at Stanford for simulating neural networks had a basic machine cycle of 21 microseconds and a maximum of 60,000 “digits” of random-access memory.) For adjustable weights, MINOS used magnetic devices designed by Brain. 98 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 99. 4.2 Neural Networks Rosenblatt stayed in close contact with SRI because he was interested in using these magnetic devices as replacements for his motor-driven potentiometers. Figure 4.7: MINOS. Note the input switches and corresponding indicator lights in the second-from-the-left rack of equipment. The magnetic weights are at the top of the third rack. (Photograph used with permission of SRI International.) Rosen’s enthusiasm and optimism about the potential for neural networks helped convince me to join SRI. Upon my arrival in July 1961, I was given a draft of Rosenblatt’s book to read. Brain’s team was just beginning work on the construction of a large neural network, called MINOS II, a follow-on system to the smaller MINOS. (See Fig. 4.8.) Work on the MINOS systems was supported primarily by the U.S. Army Signal Corps during the period 1958 to 1967. The objective of the MINOS work was “to conduct a research study and experimental investigation of techniques and equipment characteristics suitable for practical application to graphical data processing for military requirements.” The main focus of the project was the automatic recognition of symbols on military maps. Other applications – such as the recognition of military vehicles, such as tanks, on aerial photographs and the recognition of hand-printed characters – were also Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 99
- 100. 4 Pattern Recognition attempted.15 In the first stage of processing by MINOS II, the input image was replicated 100 times by a 10 × 10 array of plastic lenses. Each of these identical images was then sent through its own optical feature-detecting mask, and the light through the mask was detected by a photocell and compared with a threshold. The result was a set of 100 binary (off–on) values. These values were the inputs to a set of 63 neural elements (“A-units” in Rosenblatt’s terminology), each with 100 variable magnetic weights. The 63 binary outputs from these neural elements were then translated into one of 64 decisions about the category of the original input image. (We constructed 64 equally distant “points” in the sixty-three-dimensional space and trained the neural network so that each input image produced a point closer to its own prototype point than to any other. Each of these prototype points was one of the 64 “maximal-length shift-register sequences” of 63 dimensions.)16 Figure 4.8: MINOS II: operator’s display board (left), an individual weight frame (middle), and weight frames with logic circuitry (right). (Photographs used with permission of SRI International.) During the 1960s, the SRI neural network group, by then called the Learning Machines Group, explored many different network organizations and training procedures. As computers became both more available and more powerful, we increasingly used simulations (at various computer centers) on the Burroughs 220 and 5000 and on the IBM 709 and 7090. In the mid-1960s, we obtained our own dedicated computer, an SDS 910. (The SDS 910, developed at Scientific Data Systems, was the first computer to use silicon transistors.) We used that computer in conjunction with the latest version of our neural network hardware (now using an array of 1,024 preprocessing lenses), a combination we called MINOS III. 100 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 101. 4.2 Neural Networks One of the most successful results with the MINOS III system was the automatic recognition of hand-printed characters on FORTRAN coding sheets. (In the 1960s, computer programs were typically written by hand and then converted to punched cards by key-punch operators.) This work was led by John Munson (1939–1972; Fig. 4.9), Peter Hart (1941– ; Fig. 4.9), and Richard Duda (1936– ; Fig. 4.9). The neural net part of MINOS III was used to produce a ranking of the possible classifications for each character with a confidence measure for each. For example, the first character encountered in a string of characters might be recognized by the neural net as a “D” with a confidence of 90 and as an “O” with a confidence of 10. But accepting the most confident decision for each character might not result in a string that is a legal statement in the FORTRAN language – indicating that one or more of the decisions was erroneous (where it is assumed that whoever wrote statements on the coding sheet wrote legal statements). Accepting the second or third most confident choices for some of the characters might be required to produce a legal string. Figure 4.9: John Munson (left), Peter Hart (middle), and Richard Duda (right). (Photographs courtesy of Faith Munson, of Peter Hart, and of Richard Duda.) The overall confidence of a complete string of characters was calculated by adding the confidences of the individual characters in the string. Then, what was needed was a way of ranking these overall confidence numbers for each of the possible strings resulting from all of the different choices for each character. Among this ranking of all possible strings, the system then selected the most confident legal string. As Richard Duda wrote, however, “The problem of finding the 1st, 2nd, 3rd,. . . most confident string of characters is by no means a trivial problem.” The key to computing the ranking in an efficient manner was to use a method called dynamic programming.17 (In a later chapter, we’ll see dynamic programming used again in speech recognition systems.) An illustration of a sample of the original source and the final output is Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 101
- 102. 4 Pattern Recognition shown in Fig. 4.10. Figure 4.10: Recognition of FORTRAN characters. Input is above and output (with only two errors) is below. (Illustration used with permission of SRI Inter- national.) After the neural net part of the system was trained, the overall system (which decided on the most confident legal string) was able to achieve a recognition accuracy of just over 98% on a large sample of material that was not part of what the system was trained on. Recognizing handwritten characters with this level of accuracy was a significant achievement in the 1960s.18 Expanding its interests beyond neural networks, the Learning Machines Group ultimately became the SRI Artificial Intelligence Center, which continues today as a leading AI research enterprise. 4.3 Statistical Methods During the 1950s and 1960s there were several applications of statistical methods to pattern-recognition problems. Many of these methods bore a close resemblance to some of the neural network techniques. Recall that earlier I explained how to decide which of two tones was present in a noisy radio signal. A similar technique could be used for pattern recognition. For classifying images (or other perceptual inputs), it was usual to represent the input by a 102 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 103. 4.3 Statistical Methods list of distinguishing “features” such as those used by Selfridge and his colleagues. In alphanumeric character recognition for example, one first extracted features from the image of the character to be classified. Usually the features had numerical values, such as the number of times lines of different angles intersected the character or the length of the perimeter of the smallest circle that completely enclosed the character. Selecting appropriate features was often more art than science, but it was critical to good performance. We’ll need a bit of elementary mathematical notation to help describe these statistically oriented pattern-recognition methods. Suppose the list of features extracted from a character is {f1, f2, . . . , fi, . . . , fN }. I’ll abbreviate this list by the bold-face symbol X. Suppose there are k categories, C1, C2, . . . , Ci, . . . , Ck to which the character described by X might belong. Using Bayes’s rule in a manner similar to that described earlier, the decision rule is the following: Decide in favor of that category for which p(X | Ci)p(Ci) is largest, where p(Ci) is the a priori probability of category Ci and p(X | Ci) is the likelihood of X given Ci. These likelihoods can be inferred by collecting statistical data from a large sample of characters. As I mentioned earlier, researchers in pattern recognition often describe the decision process in terms of geometry. They imagine that the values of the features obtained from an image sample can be represented as a point in a multidimensional space. If we have several samples for each of, say, two known categories of data, we can represent these samples as scatterings of points in the space. In character recognition, scattering can occur not only because the image of the character might be noisy but also because characters in the same category might be drawn slightly differently. I show a two-dimensional example, with features f1 and f2, in Fig. 4.11. From the scattering of points in each category we can compute an estimate of the probabilities needed for computing likelihoods. Then, we can use the likelihoods and the prior probabilities to make decisions. I show in this figure the boundary, computed from the likelihoods and the prior probabilities, that divides the space into two regions. In one region, we decide in favor of category 1; in the other, we decide in favor of category 2. I also show a new feature point, X, to be classified. In this case, the position of X relative to the boundary dictates that we classify X as a member of category 1. There are other methods also for classifying feature points. An interesting example is the “nearest-neighbor” method. In that scheme, invented by E. Fix and J. L. Hodges in 1951,19 a new feature point is assigned to the same category as that sample feature point to which it is closest. In Fig. 4.11, the new point X would be classified as belonging to category 2 using the nearest-neighbor method. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 103
- 104. 4 Pattern Recognition Figure 4.11: A two-dimensional space of feature points and a separating bound- ary. An important elaboration of the nearest-neighbor method assigns a new point to the same category as the majority of the k closest points. Such a decision rule seems plausible (in the case in which there are many, many sample points of each category) because there being more sample points of category Ci closer to an unknown point, X, than sample points of category Cj is evidence that p(X | Ci)p(Ci) is greater than p(X | Cj)p(Cj). Expanding on that general observation, Thomas Cover and Peter Hart rigorously analyzed the performance of nearest-neighbor methods.20 Any technique for pattern recognition, even those using neural networks or nearest neighbors, can be thought of as constructing separating boundaries in a multidimensional space of features. Another method for constructing boundaries using “potential functions” was suggested by the Russian scientists M. A. Aizerman, E. M. Braverman, and L. I. Rozonoer in the 1960s.21 Some important early books on the use of statistical methods in pattern recognition are ones by George Sebestyen,22 myself,23 and Richard Duda and Peter Hart.24 My book also describes some of the relationships between statistical methods and those based on neural networks. The technology of pattern recognition as of the late 1960s is nicely reviewed by George Nagy (who had earlier been one of Frank Rosenblatt’s graduate students).25 104 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 105. 4.4 Applications of Pattern Recognition to Aerial Reconnaissance 4.4 Applications of Pattern Recognition to Aerial Reconnaissance The neural network and statistical methods for pattern recognition attracted much attention in many aerospace and avionics companies during the late 1950s and early 1960s. These companies had ample research and development budgets stemming from their contracts with the U.S. Department of Defense. Many of them were particularly interested in the problem of aerial reconnaissance, that is, locating and identifying “targets” in aerial photographs. Among the companies having substantial research programs devoted to this and related problems were the Aeronutronic Division of the Ford Motor Co.,26 Douglas Aircraft Company (as it was known at that time), General Dynamics, Lockheed Missiles and Space Division, and the Philco Corporation. (Philco was later acquired by Ford in late 1961.) I’ll mention some of the work at Philco as representative. There, Laveen N. Kanal (1931– ), Neil C. Randall (1930– ), and Thomas Harley (1929– ) worked on both the theory and applications of statistical pattern-recognition methods. The systems they developed were for screening aerial photographs for interesting military targets such as tanks. A schematic illustration of one of their systems is shown in Fig. 4.12.27 Philco’s apparatus scanned material from 9-inch film negatives gathered by a U2 reconnaissance airplane during U.S. Army tank maneuvers at Fort Drum, New York. A small section of the scanned photograph, possibly containing an M-48 tank (in standard position and size), was first processed to enhance edges, and the result was presented to the target detection system as an array of 1’s and 0’s. The first of their systems used a 22 × 12 array; later ones used a 32 × 32 array as shown in Fig. 4.12. The array was then segmented into 24 overlapping 8 × 8 “feature blocks.” The data in each feature block were then subjected to a statistical test to decide whether or not the small area of the picture represented by this block contained part of a tank. The statistical tests were based on a “training sample” of 50 images containing tanks and 50 samples of terrain not containing tanks. For each 8 × 8 feature block, statistical parameters were compiled from these samples to determine a (linear) boundary in the sixty-four-dimensional space that best discriminated the tank samples from the nontank samples. Using these boundaries, the system was then tested on a different set of 50 images containing tanks and 50 images not containing tanks. For each test image, the number of feature blocks deciding “tank present” was tallied to produce a final numerical “score” (such as 21 out of the 24 blocks decided a tank was present). This score could then be used to decide whether or not the image contained a tank. The authors stated that “the experimental performance of the statistical classification procedure exceeded all expectations.” Almost half of the test Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 105
- 106. 4 Pattern Recognition Figure 4.12: A Philco tank-recognition system. (Adapted from Laveen N. Kanal and Neal C. Randall, “Target Detection in Aerial Photography,” paper 8.3, Pro- ceedings of the 1964 Western Electronics Show and Convention (WESCON), Los Angeles, CA, Institute of Radio Engineers (now IEEE), August 25–28, 1964.) samples had perfect scores (that is, all 24 feature blocks correctly discriminated between tank and nontank). Furthermore, all of the test samples containing tanks had a score greater than or equal to 11, and all of the test samples not containing tanks had a score less than or equal to 7. An early tank-detecting system at Philco was built with analog circuitry – not programmed on a computer. As Thomas Harley, the project leader for this system, later elaborated,28 It is important to remember the technological context of the era in which this work was done. The system we implemented had no built-in computational capabilities. The weights in the linear discriminant function were resistors that controlled the current coming from the (binary) voltage source in the shift register elements. Those currents were added together, and each feature was recognized or not depending whether on the sum of those 106 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 107. 4.4 Applications of Pattern Recognition to Aerial Reconnaissance currents exceeded a threshold value. Those binary feature decisions were then summed, again in an analog electrical circuit, not in a computer, and again a decision [tank or no tank] was made depending on whether the sum exceeded a threshold value. In another system, the statistical classification was implemented by a program, called MULTINORM, running on the Philco 2000 computer.29 In other experiments Philco used additional statistical tests to weight some of the feature blocks more heavily than others in computing the final score. Kanal told me that these experiments with weighting the outputs of the feature blocks “anticipated the support vector machine (SVM) classification idea. . . [by] using the first layer to identify the training samples close to the boundary between tanks and non-tanks.”30 (I’ll describe the important SVM method in a later chapter.) Of course, these systems had a rather easy task. All of the tanks were in standard position and were already isolated in the photograph. (The authors mention, however, how the system could be adapted to deal with tanks occurring in any position or orientation in the image.) The photograph in Fig. 4.13 shows a typical tank image. (The nontank images are similar, except without the tank.) I find the system interesting not only because of its performance but also because it is a layered system (similar to Pandemonium and to the alpha-perceptron) and because it is an example in which the original image is divided into overlapping subimages, each of which is independently processed. As I’ll mention later, overlapping subimages play a prominent role in some computational models of the neocortex. Unfortunately, the Philco reports giving details of this work aren’t readily available.31 Furthermore, Philco and some of the other groups engaged in this work have disappeared. Here is what Tom Harley wrote me about the Philco reports and about Philco itself:32 Most of the pattern recognition work done at Philco in the 1960s was sponsored by the DoD [Department of Defense], and the reports were not available for public distribution. Since then, the company itself has really vanished into thin air. Philco was bought by Ford Motor Company in 1961, and by 1966, they had eliminated the Philco research labs where Laveen [Kanal] and I were working. Ford tried to move our small pattern recognition group to Newport Beach, CA [the location of Ford’s Aeronutronic Division, whose pattern recognition group folded later also], and when we all decided not to go, they transferred us to their Communications Division, and told us to close out our pattern recognition projects. Laveen eventually went off to the University of Maryland, and in 1975, I transferred to the Ford Aerospace Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 107
- 108. 4 Pattern Recognition Figure 4.13: A typical tank image. (Photograph courtesy of Thomas Harley.) Western Development Labs (WDL) in Palo Alto, where I worked on large systems for the intelligence community. In later years, what had been Philco was sold to Loral, and most of that was later sold to Lockheed Martin. I retired from Lockheed in 2001. Approaches to AI problems involving neural networks and statistical techniques came to be called “nonsymbolic” to contrast them with the “symbol-processing” work being pursued by those interested in proving theorems, playing games, and problem solving. These nonsymbolic approaches found application mainly in pattern recognition, speech processing, and computer vision. Workshops and conferences devoted especially to those topics began to be held in the 1960s. A subgroup of the IEEE Computer Society (the Pattern Recognition Subcommittee of the Data Acquisition and 108 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 109. 4.4 NOTES Transformation Committee) organized the first “Pattern Recognition Workshop,” which was held in Puerto Rico in October 1966.33 A second one (which I attended) was held in Delft, The Netherlands, in August 1968. In 1966, this subgroup became the IEEE Computer Society Pattern Analysis and Machine Intelligence (PAMI) Technical Committee, which continued to organize conferences and workshops.34 Meanwhile, during the late 1950s and early 1960s, the symbol-processing people did their work mainly at MIT, at Carnegie Mellon University, at IBM, and at Stanford University. I’ll turn next to describing some of what they did. Notes 1. See http://www.nist.gov/public affairs/techbeat/tb2007 0524.htm. [89] 2. Russell A. Kirsch et al., “Experiments in Processing Pictorial Information with a Digital Computer,” Proceedings of the Eastern Joint Computer Conference, pp. 221–229, Institute of Radio Engineering and Association for Computing Machinery, December 1957. [89] 3. The proceedings of the conference were published in George L. Fischer Jr. et al., Optical Character Recognition, Washington, DC: Spartan Books, 1962. [90] 4. From J. Rabinow, “Developments in Character Recognition Machines at Rabinow Engineering Company,” in George L. Fischer Jr. et al., op. cit., p. 27. [90] 5. From http://www.sri.com/about/timeline/erma-micr.html. [90] 6. Oliver G. Selfridge and Ulrich Neisser, “Pattern Recognition by Machine,” Scientific American, Vol. 203, pp. 60–68, 1960. (Reprinted in Edward A. Feigenbaum and Julian Feldman (eds.), Computers and Thought, pp. 237ff, New York: McGraw Hill, 1963.) [91] 7. An early reference is Frank Rosenblatt, “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain,” Psychological Review, Vol. 65, pp. 386ff, 1958. [92] 8. H. David Block, “The Perceptron: A Model for Brain Functioning,” Reviews of Modern Physics, Vol. 34, No. 1, pp. 123–135, January 1962. [97] 9. Albert B. J. Novikoff, “On Convergence Proofs for Perceptrons,” in Proceedings of the Symposium on Mathematical Theory of Automata, pp. 615–622, Brooklyn, NY: Polytechnic Press of Polytechnic Inst. of Brooklyn, 1963. [97] 10. Nils J. Nilsson, Learning Machines: Foundations of Trainable Pattern-Classifying Systems, New York: McGraw-Hill Book Co., 1965; republished as The Mathematical Foundations of Learning Machines, San Francisco: Morgan Kaufmann Publishers, 1990. [97] 11. Frank Rosenblatt, Principles of Neurodynamics, Washington, DC: Spartan Books, 1962. [97] 12. Frank Rosenblatt, “A Description of the Tobermory Perceptron,” Collected Technical Papers, Vol. 2, Cognitive Systems Research Program, Cornell University, 1963. [97] 13. Woodrow W. Bledsoe and Iben Browning, “Pattern Recognition and Reading by Machine,” Proceedings of the Eastern Joint Computer Conference, pp. 225–232, New York: Association for Computing Machinery, 1959. [97] 14. William C. Ridgway, “An Adaptive Logic System with Generalizing Properties,” Stanford Electronics Laboratories Technical Report 1556-1, Stanford University, Stanford, CA, 1962. [98] Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 109
- 110. 4 NOTES 15. For a description of MINOS II, see Alfred E. Brain, George Forsen, David Hall, and Charles Rosen, “A Large, Self-Contained Learning Machine,” Proceedings of the Western Electronic Show and Convention, 1963. The paper was reprinted as Appendix C of an SRI proposal and is available online at http://www.ai.sri.com/pubs/files/rosen65-esu65-1tech.pdf. [100] 16. For a discussion of shift-register codes and other codes, see W. Peterson, Error-Correcting Codes, New York: John Wiley & Sons, 1961. Our technique was reported in A. E. Brain and N. J. Nilsson, “Graphical Data Processing Research Study and Experimental Investigation,” Quarterly Progress Report No. 8, p. 11, SRI Report, June 1962; available online at http://www.ai.sri.com/pubs/files/1329.pdf. [100] 17. Robert E. Larsen of SRI suggested using this method. The online encyclopedia Wikipedia has a clear description of dynamic programming. See http://en.wikipedia.org/wiki/Dynamic programming. [101] 18. The technical details of the complete system are described in two papers: John Munson, “Experiments in the Recognition of Hand-Printed Text: Part I – Character Recognition,” and Richard O. Duda and Peter E. Hart, “Experiments in the Recognition of Hand-Printed Text: Part II – Context Analysis,” AFIPS Conference Proceedings, (of the 1968 Fall Joint Computer Conference), Vol. 33, pp. 1125–1149, Washington, DC: Thompson Book Co., 1968. Additional information can be found in SRI AI Center Technical reports, available online at http://www.ai.sri.com/pubs/files/1343.pdf and http://www.ai.sri.com/pubs/files/1344.pdf. [102] 19. E. Fix and J. L. Hodges Jr., “Discriminatory analysis, nonparametric discrimination,” USAF School of Aviation Medicine, Randolph Field, Texas, Project 21-49-004, Report 4, Contract AF41(128)-31, February 1951. See also B. V. Dasarathy (ed.), Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques, Los Alamitos, CA: IEEE Computer Society Press, which is a reprint of 1951 unpublished work of Fix and Hodges. [103] 20. Thomas M. Cover and Peter E. Hart, “Nearest Neighbor Pattern Classification,” IEEE Transactions on Information Theory, pp. 21–27, January 1967. Available online at http://ieeexplore.ieee.org/iel5/18/22633/01053964.pdf. [104] 21. See M. A. Aizerman, E. M. Braverman, and L. I. Rozonoer, “Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning,” Automation and Remote Control, Vol. 25, pp. 917–936, 1964, and A. G. Arkadev and E. M. Braverman, Computers and Pattern Recognition, (translated from the Russian by W. Turski and J. D. Cowan), Washington, DC: Thompson Book Co., Inc., 1967. [104] 22. George S. Sebestyen, Decision-Making Processes in Pattern Recognition, Indianapolis, IN: Macmillan Publishing Co., Inc., 1962. [104] 23. Nils J. Nilsson, op. cit. [104] 24. Richard O. Duda and Peter E. Hart, Pattern Classification and Scene Analysis, New York: John Wiley & Sons, 1973; updated version: Richard O. Duda, Peter E. Hart, and David G. Stork, Pattern Classification, 2nd Edition, New York: John Wiley & Sons, 2000. [104] 25. George Nagy, “State of the Art in Pattern Recognition,” Proceedings of the IEEE, Vol. 56, No. 5, pp. 836–857, May 1968. [104] 26. See, for example, Joseph K. Hawkins and C. J. Munsey, “An Adaptive System with Direct Optical Input,” Proceedings of the IEEE, Vol. 55, No. 6, pp. 1084–1085, June 1967. Available online for IEEE members at http://ieeexplore.ieee.org/iel5/5/31078/01446273. pdf?tp=&arnumber=1446273&isnumber=31078. [105] 27. Laveen N. Kanal and Neal C. Randall, “Target Detection in Aerial Photography,” paper 8.3, Proceedings of the 1964 Western Electronics Show and Convention (WESCON), Los Angeles, CA, Institute of Radio Engineers (now IEEE), August 25–28, 1964. (Several other 110 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 111. 4.4 NOTES papers on pattern recognition were presented at this conference and are contained in the proceedings.) [105] 28. Thomas Harley, personal e-mail communication, July 15, 2007. [106] 29. Laveen N. Kanal and Neal C. Randall, op. cit.. [107] 30. Laveen Kanal, personal e-mail communication, July 13, 2007. [107] 31. Laveen N. Kanal, “Statistical Methods for Pattern Classification,” Philco Report, 1963; originally appeared in T. Harley et al., “Semi-Automatic Imagery Screening Research Study and Experimental Investigation,” Philco Reports VO43-2 and VO43-3, Vol. I, Sec. 6, and Appendix H, prepared for U.S. Army Electronics Research and Development Laboratory under Contract DA-36-039-SC- 90742, March 29, 1963. [107] 32. Thomas Harley, personal e-mail communication, July 11, 2007. [107] 33. Laveen N. Kanal (ed.), Pattern Recognition, Proceedings of the IEEE Workshop on Pattern Recognition, held at Dorado, Puerto Rico, Washington, DC: Thompson Book Co., 1968. [109] 34. See the Web page at http://tab.computer.org/pamitc/. [109] Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 111
- 112. 4 NOTES 112 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 113. 5.1 Chapter 5 Early Heuristic Programs 5.1 The Logic Theorist and Heuristic Search Just prior to the Dartmouth workshop, Newell, Shaw, and Simon had programmed a version of LT on a computer at the RAND Corporation called the JOHNNIAC (named in honor of John von Neumann). Later papers1 described how it proved some of the theorems in symbolic logic that were proved by Russell and Whitehead in Volume I of their classic work, Principia Mathematica.2 LT worked by performing transformations on Russell and Whitehead’s five axioms of propositional logic, represented for the computer by “symbol structures,” until a structure was produced that corresponded to the theorem to be proved. Because there are so many different transformations that could be performed, finding the appropriate ones for proving the given theorem involves what computer science people call a “search process.” To describe how LT and other symbolic AI programs work, I need to explain first what is meant by a “symbol structure” and what is meant by “transforming” them. In a computer, symbols can be combined in lists, such as (A, 7, Q). Symbols and lists of symbols are the simplest kinds of symbol structures. More complex structures are composed of lists of lists of symbols, such as ((B, 3), (A, 7, Q)), and lists of lists of lists of symbols, and so on. Because such lists of lists, etc. can be quite complex, they are called “structures.” Computer programs can be written that transform symbol structures into other symbol structures. For example, with a suitable program the structure “(the sum of seven and five)” could be transformed into the structure “(7 + 5),” which could further be transformed into the symbol “12.” Transforming structures of symbols and searching for an appropriate problem-solving sequence of transformations lies at the heart of Newell and Simon’s ideas about mechanizing intelligence. In a later paper (the one they gave on the occasion of their receiving the prestigious Turing Award), they Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 113
- 114. 5 Early Heuristic Programs summarized the process as follows:3 The solutions to problems are represented as symbol structures. A physical symbol system exercises its intelligence in problem solving by search – that is, by generating and progressively modifying symbol structures until it produces a solution structure. . . . To state a problem is to designate (1) a test for a class of symbol structures (solutions of the problem), and (2) a generator of symbol structures (potential solutions). To solve a problem is to generate a structure, using (2), that satisfies the test of (1). Understanding in detail how LT itself used symbol structures and their transformations to prove theorems would require some mathematical and logical background. The process is easier to explain by using one of AI’s favorite “toy problems” – the “fifteen-puzzle.” (See Fig. 5.1.) The fifteen-puzzle is one of several types of sliding-block puzzles. The problem is to transform an array of tiles from an initial configuration into a “goal” configuration by a succession of moves of a tile into an adjacent empty cell. Figure 5.1: Start (left) and goal (right) configurations of a fifteen-puzzle prob- lem. I’ll use a simpler version of the puzzle – one that uses a 3 × 3 array of eight sliding tiles instead of the 4 × 4 array. (AI researchers have experimented with programs for solving larger versions of the puzzle also, such as 5 × 5 and 6 × 6.) Suppose we wanted to move the tiles from their configuration on the left to the one on the right as illustrated in Fig. 5.2. Following the Newell and Simon approach, we must first represent tile positions for the computer by symbol structures that the computer can deal 114 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 115. 5.1 The Logic Theorist and Heuristic Search Figure 5.2: The eight-puzzle. with. I will represent the starting position by the following structure, which is a list of three sublists: ((2, 8, 3), (1, 6, 4), (7, B, 5)). The first sublist, namely, (2, 8, 3), names the occupants of the first row of the puzzle array, and so on. B stands for the empty cell in the middle of the third row. In the same fashion, the goal configuration is represented by the following structure: ((1, 2, 3), (8, B, 4), (7, 6, 5)). Next, we have to show how a computer can transform structures of the kind we have set up in a way that corresponds to the allowed moves of the puzzle. Note that when a tile is moved, it swaps places with the blank cell; that is, the blank cell moves too. The blank cell can either move within its row or can change rows. Corresponding to these moves of the blank cell, when a tile moves within its row, B swaps places with the number either to its left in its list (if there is one) or to its right (if there is one). A computer can easily make either of these transformations. When the blank cell moves up or down, B swaps places with the number in the corresponding position in the list to the left (if there is one) or in the list to the right (if there is one). These transformations can also be made quite easily by a computer program. Using the Newell and Simon approach, we start with the symbol structure representing the starting configuration of the eight-puzzle and apply allowed transformations until a goal is reached. There are three transformations of the starting symbol structure. These produce the following structures: ((2, 8, 3), (1, 6, 4), (B, 7, 5)), ((2, 8, 3), (1, 6, 4), (7, 5, B)), and ((2, 8, 3), (1, B, 4), (7, 6, 5)). Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 115
- 116. 5 Early Heuristic Programs None of these represents the goal configuration, so we continue to apply transformations to each of these and so on until a structure representing the goal is reached. We (and the computer) can keep track of the transformations made by arranging them in a treelike structure such as shown in Fig. 5.3. (The arrowheads on both ends of the lines representing the transformations indicate that each transformation is reversible.) Figure 5.3: A search tree. This version of the eight-puzzle is relatively simple, so not many transformations have to be tried before the goal is reached. Typically though (especially in larger versions of the puzzle), the computer would be swamped by all of the possible transformations – so much so that it would never generate a goal expression. To constrain what was later called “the combinatorial explosion” of transformations, Newell and Simon suggested using “heuristics” to generate only those transformations guessed as likely to be on the path to a solution. In one of their papers about LT, they wrote “A process that may solve a problem, but offers no guarantees of doing so, is called a heuristic for that problem.” Rather than blindly striking out in all directions in a search for a 116 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 117. 5.1 The Logic Theorist and Heuristic Search proof, LT used search guided by heuristics, or “heuristic search.” Usually, as was the case with LT, there is no guarantee that heuristic search will be successful, but when it is successful (and that is quite often) it eliminates much otherwise fruitless search effort. The search for a solution to an eight-puzzle problem involves growing the tree of symbol structures by applying transformations to the “leaves” of the tree and thus extending it. To limit the growth of the tree, we should use heuristics to apply transformations only to those leaves thought to be on the way to a solution. One such heuristic might be to apply a transformation to that leaf with the smallest number of tiles out of position compared to the goal configuration. Because sliding tile problems have been thoroughly studied, there are a number of heuristics that have proved useful – ones much better than the simple number-of-tiles-out-of-position one I have just suggested. Using heuristics keyed to the problem being solved became a major theme in artificial intelligence, giving rise to what is called “heuristic programming.” Perhaps the idea of heuristic search was already “in the air” around the time of the Dartmouth workshop. It was implicit in earlier work by Claude Shannon. In March 1950, Shannon, an avid chess player, published a paper proposing ideas for programming a computer to play chess.4 In his paper, Shannon distinguished between what he called “type A” and “type B” strategies. Type A strategies examine every possible combination of moves, whereas type B strategies use specialized knowledge of chess to focus on lines of play thought to be the most productive. The type B strategies depended on what Newell and Simon later called heuristics. And Minsky is quoted as saying “. . . I had already considered the idea of heuristic search obvious and natural, so that the Logic Theorist was not impressive to me.”5 It was recognized quite early in AI that the way a problem is set up, its “representation,” is critical to its solution. One example of how a representation affects problem solving is due to John McCarthy and is called the “mutilated checkerboard” problem.6 Here’s the problem: “Two diagonally opposite corner squares are removed from a checkerboard. Is it possible to cover the remaining squares with dominoes?” (A domino is a rectangular tile that covers two adjacent squares.) A naive way of searching for a solution would be to try to place dominoes in all possible ways over the checkerboard. But, if one uses the information that a checkerboard consists of 32 squares of one color and 32 of another color, and that the opposite corner squares are of the same color, then one realizes that the mutilated board consists of 30 squares of one color and 32 of another. Because a domino covers two squares of opposite colors, there is no way that a set of them can cover the remaining colors. McCarthy was interested in whether or not people could come up with “creative” ways to formulate the puzzle so that it could be solved by computers using methods based on logical deduction. Another classic puzzle that has been used to study the effects of different representations is the “missionary and cannibals” problem: Three cannibals Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 117
- 118. 5 Early Heuristic Programs and three missionaries must cross a river. Their boat can only hold two people. If the cannibals outnumber the missionaries, on either side of the river, the missionaries on that side perish. Each missionary and each cannibal can row the boat. How can all six get across the river safely? Most people have no trouble formulating this puzzle as a search problem, and the solution is relatively easy. But it does require making one rather nonintuitive step. The computer scientist and AI researcher Saul Amarel (1928–2002) wrote a much-referenced paper analyzing this puzzle and various extended versions of it in which there can be various numbers of missionaries and cannibals.7 (The extended versions don’t appear to be so easy.) After moving from one representation to another, Amarel finally developed a representation for a generalized version of the problem whose solution required virtually no search. AI researchers are still studying how best to represent problems and, most importantly, how to get AI systems to come up with their own representations. 5.2 Proving Theorems in Geometry Nathan Rochester returned to IBM after the Dartmouth workshop excited about discussions he had had with Marvin Minsky about Minsky’s ideas for a possible computer program for proving theorems in geometry. He described these ideas to a new IBM employee, Herb Gelernter (1929– ). Gelernter soon began a research project to develop a geometry-theorem-proving machine. He presented a paper on the first version of his program at a conference in Paris in June 1959,8 acknowledging that [t]he research project itself is a consequence of the Dartmouth Summer Research Project on Artificial Intelligence held in 1956, during which M. L. Minsky pointed out the potential utility of the diagram to a geometry theorem-proving machine. Gelernter’s program exploited two important ideas. One was the explicit use of subgoals (sometimes called “reasoning backward” or “divide and conquer”), and the other was the use of a diagram to close off futile search paths. The strategy taught in high school for proving a theorem in geometry involves finding some subsidiary geometric facts from which, if true, the theorem would follow immediately. For example, to prove that two angles are equal, it suffices to show that they are corresponding angles of two “congruent” triangles. (A triangle is congruent to another if it can be translated and rotated, possibly even flipped over, in such a way that it matches the other exactly.) So now, the original problem is transformed into the problem of showing that two triangles are congruent. One way (among others) to show that two triangles are congruent is to show that two corresponding sides and the enclosed angle of the two triangles all have the 118 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 119. 5.2 Proving Theorems in Geometry same sizes. This backward reasoning process ends when what remains to be shown is among the premises of the theorem. Readers familiar with geometry will be able to follow the illustrative example shown in Fig. 5.4. There, on the left-hand side, we are given triangle ABC with side AB equal to side AC and must prove that angle ABC is equal to angle ACB. The triangle on the right side is a flipped-over version of triangle ABC. Figure 5.4: A triangle with two equal sides (left) and its flipped-over version (right). Here is how the proof goes: If we could prove that triangle ABC is congruent to triangle B C A , then the theorem would follow because the two angles are corresponding angles of the two triangles. These two triangles can be proved congruent if we could establish that side AB (of triangle ABC) is equal to side AC (of triangle B C A ) and that side AC (of triangle ABC) is equal to side B A (of triangle B C A ) and that angle A (of triangle ABC) is equal to angle A (of triangle B C A ). But the premises state that side AB is equal to side AC, and these lengths don’t change in the flipped-over triangle. Similarly, angle A is equal to its flipped-over version – so we have our proof. Before continuing my description of Gelernter’s program, a short historical digression is in order. The geometry theorem just proved is famous – being the fifth proposition in Book I of Euclid’s Elements. Because Euclid’s proof of the proposition was a difficult problem for beginners it became known as the pons asinorum or “fools bridge.” The proof given here is simpler than Euclid’s – a version of it was given by Pappus of Alexandria (circa 290–350 ce). Minsky’s “hand simulation” of a program for proving theorems in geometry, discussed at Dartmouth, came up with this very proof (omitting what I think is the helpful step of flipping the triangle over). Minsky wrote9 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 119
- 120. 5 Early Heuristic Programs In 1956 I wrote two memos about a hand-simulated program for proving theorems in geometry. In the first memo, the procedure found the simple proof that if a triangle has two equal sides then the corresponding angles are equal. It did this by noticing that triangle ABC was congruent to triangle CBA because of “side-angle-side.” What was interesting is that this was found after a very short search – because, after all, there weren’t many things to do. You might say the program was too stupid to do what a person might do, that is, think, “Oh, those are both the same triangle. Surely no good could come from giving it two different names.” (The program has a collection of heuristic methods for proving Euclid-Like theorems, and one was that “if you want to prove two angles are equal, show that they’re corresponding parts of congruent triangles.” Then it also had several ways to demonstrate congruence. There wasn’t much more in that first simulation.) But I can’t find that memo anywhere. As Minsky said, this is a very easy problem for a computer. Gelernter’s program proved much more difficult theorems, and for these his use of a diagram was essential. The program did not literally draw and look at a diagram. Instead, as Gelernter wrote, [The program is] supplied with the diagram in the form of a list of possible coordinates for the points named in the theorem. This point list is accompanied by another list specifying the points joined by segments. Coordinates are chosen to reflect the greatest possible generality in the figures. So, for example, the points named in the problem about proving two angles equal are the vertices of the triangle ABC, namely, points A and B and C. Coordinates for each of these points are chosen, and care is taken to make sure that these coordinates do not happen to satisfy any special unnamed properties. Gelernter’s program worked by setting up subgoals and subsubgoals such as those I used in the example just given. It then searched for a chain of these ending in subgoals that could be established directly from the premises. Before any subgoal was selected by the program to be worked on however, it was first tested to see whether it held in the diagram. If it did hold, it might possibly be provable and could therefore be considered as a possible route to a proof. But, if it did not hold in the diagram, it could not possibly be true. Thus, it could be eliminated from further consideration, thereby “pruning” the search tree and saving what would certainly be fruitless effort. Later work in AI would also exploit “semantic” information of this sort. We can see similarities between the strategies used in the geometry program and those used by humans when we solve problems. It is common for 120 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 121. 5.3 The General Problem Solver us to work backward – transforming a hard problem into subproblems and those into subsubproblems and so on until finally the problems are trivial. When a subproblem has many parts, we know that we must solve all of them. We also recognize when a proposed subproblem is patently impossible and thus can reject it. The next program I describe was based explicitly on what its authors thought were human problem-solving strategies. 5.3 The General Problem Solver At the same 1959 Paris conference where Gelernter presented his program, Allen Newell, J. C. Shaw, and Herb Simon gave a paper describing their recent work on mechanizing problem solving.10 Their program, which they called the “General Problem Solver (GPS),” was an embodiment of their ideas about how humans solve problems. Indeed, they claimed that the program itself was a theory of human problem-solving behavior. Newell and Simon were among those who were just as interested (perhaps even more interested) in explaining the intelligent behavior of humans as they were in building intelligent machines. They wrote “It is often argued that a careful line must be drawn between the attempt to accomplish with machines the same tasks that humans perform, and the attempt to simulate the processes humans actually use to accomplish these tasks. . . . GPS maximally confuses the two approaches – with mutual benefit.”11 GPS was an outgrowth of their earlier work on the Logic Theorist in that it was based on manipulating symbol structures (which they believed humans did also). But GPS had an important additional mechanism among its symbol-manipulating strategies. Like Gelernter’s geometry program, GPS transformed problems into subproblems, and so on. GPS’s innovation was to compute a “difference” between a problem to be solved (represented as a symbol structure) and what was already known or given (also represented as a symbol structure). The program then attempted to reduce this difference by applying some symbol-manipulating “operator” (known to be relevant to this difference) to the initial symbol structure. Newell and Simon called this strategy “means–ends analysis.” (Note the similarity to feedback control systems, which continuously attempt to reduce the difference between a current setting and a desired setting.) To do so, it would have to show that the operator’s applicability condition was satisfied – a subproblem. The program then started up another version of itself to work on this subproblem, looking for a difference and so on. For example, suppose the goal is to have Sammy at school when Sammy is known to be at home.12 GPS computes a “difference,” namely, Sammy is in the wrong place, and it finds an operator relevant to reducing this difference, namely, driving Sammy to school. To drive Sammy to school requires that the car be in working order. To make the problem interesting, we’ll suppose that Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 121
- 122. 5 Early Heuristic Programs the car’s battery is dead, so GPS can’t apply the drive-car operator because that operator requires a working battery. Getting a working battery is a subproblem to which GPS can apply a version of itself. This “lower” version of GPS computes a difference, namely, the need for a working battery, and it finds an operator, namely, calling a mechanic to come and install a new battery. To call a mechanic requires having a phone number (and let us suppose we have it), so GPS applies the call-mechanic operator, resulting in the mechanic coming to install a new battery. The lower version of GPS has successfully solved its problem, so the superordinate GPS can now resume – noting that the condition for drive-car, namely, having a working battery, is satisfied. So GPS applies this operator, Sammy gets to school, and the original problem is solved. (This example illustrates the general workings of GPS. A real one using actual symbol structures, differences, and operators with their conditions and so on would be cumbersome but not more revealing.) When GPS works on subproblems by starting up a new version of itself, it uses a very important idea in computer science (and in mathematics) called “recursion.” You might be familiar with the idea that computer programmers organize complex programs hierarchically. That is, main programs fire up subprograms, which might fire-up subsubprograms, and so on. When a main program “calls” a subprogram, the main program suspends itself until the subprogram completes what it is supposed to do (possibly handing back data to the main program), and then the main program resumes work. In AI (and in other applications also), it is common to have a main program call a version of itself – taking care that the new version works on a simpler problem so as to avoid endless repetition and “looping.” Having a program call itself is called “recursion.” Do people use subprograms and recursion in their own thinking? Quite possibly, but their ability to recall how to resume what some higher level thought process was doing when that process starts up a chain of lower level processes is certainly limited. I don’t believe that GPS attempted to mimic this limitation of human thinking. Newell and Simon believed that the methods used by GPS could be used to solve a wide variety of different problems, thus giving rise to the term “general.” To apply it to a specific problem, a “table of differences” for that problem would have to be supplied. The table would list all the possible differences that might arise and match them to operators, which, for that problem, would reduce the corresponding differences. GPS was, in fact, applied to a number of different logical problems and puzzles13 and inspired later work in both artificial intelligence and in cognitive science. Its longevity as a problem-solving program itself and as a theory of human problem solving was short, however, and lives on only through its various descendants (about which more will be discussed later). Heuristic search procedures were used in a number of AI programs developed in the early 1960s. For example, another one of Minsky’s Ph.D. 122 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 123. 5.4 Game-Playing Programs students, James Slagle, programmed a system called SAINT that could solve calculus problems, suitably represented as symbol structures. It solved 52 of 54 problems taken from MIT freshman calculus final examinations.14 Much use of heuristics was used in programs that could play board games, a subject to which I now turn. 5.4 Game-Playing Programs I have already mentioned some of the early work of Shannon and of Newell, Shaw, and Simon on programs for playing chess. Playing excellent chess requires intelligence. In fact, Newell, Shaw, and Simon wrote that if “one could devise a successful chess machine, one would seem to have penetrated to the core of human intellectual endeavor.”15 Thinking about programs to play chess goes back at least to Babbage. According to Murray Campbell, an IBM researcher who helped design a world-champion chess-playing program (which I’ll mention later), Babbage’s 1845 book, The Life of a Philosopher, contains the first documented discussion of programming a computer to play chess.16 Konrad Zuse, the German designer and builder of the Z1 and Z3 computers, used his programming language called Plankalkül to design a chess-playing program in the early 1940s. In 1946 Turing mentioned the idea of a computer showing “intelligence,” with chess-playing as a paradigm.17 In 1948, Turing and his former undergraduate colleague, D. G. Champernowne, began writing a chess program. In 1952, lacking a computer powerful enough to execute the program, Turing played a game in which he simulated the computer, taking about half an hour per move. (The game was recorded. You can see it at http://www.chessgames.com/perl/chessgame?gid=1356927.) The program lost to a colleague of Turing, Alick Glennie; however, it is said that the program won a game against Champernowne’s wife.18 After these early programs, work on computer chess programs continued, with off-again–on-again effort, throughout the next several decades. According to John McCarthy, Alexander Kronrod, a Russian AI researcher, said “Chess is the Drosophila of AI” – meaning that it serves, better than more open-ended intellectual tasks do, as a useful laboratory specimen for research. As Minsky said, “It is not that the games and mathematical problems are chosen because they are clear and simple; rather it is that they give us, for the smallest initial structures, the greatest complexity, so that one can engage some really formidable situations after a relatively minimal diversion into programming.”19 Chess presents very difficult problems for AI, and it was not until the mid-1960s that the first competent chess programs appeared. I’ll return to discuss these in a subsequent chapter. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 123
- 124. 5 Early Heuristic Programs More dramatic early success, however, was achieved on the simpler game of checkers (or draughts as the game is known in British English). Arthur Samuel (Fig. 5.5) began thinking about programming a computer to play checkers in the late 1940s at the University of Illinois where he was a Professor of Electrical Engineering. In 1949, he joined IBM’s Poughkeepsie Laboratory and completed his first operating checkers program in 1952 on IBM’s 701 computer. The program was recoded for the IBM 704 in 1954. According to John McCarthy,20 “Thomas J. Watson Sr., the founder and President of IBM, remarked that the demonstration [of Samuel’s program] would raise the price of IBM stock 15 points. It did.” [Apparently, Samuel was not the first to write a checkers-playing program. According to the Encyclopedia Brittanica, Online, “The earliest successful AI program was written in 1951 by Christopher Strachey, later director of the Programming Research Group at the University of Oxford. Strachey’s checkers (draughts) program ran on the Ferranti Mark I computer at the University of Manchester, England. By the summer of 1952 this program could play a complete game of checkers at a reasonable speed.”]21 Samuel’s main interest in programming a computer to play checkers was to explore how to get a computer to learn. Recognizing the “time consuming and costly procedure[s]” involved in programming, Samuel wrote “Programming computers to learn from experience should eventually eliminate the need for much of this detailed programming effort.”22 Samuel’s efforts were among the first in what was to become a very important part of artificial intelligence, namely, “machine learning.” His first program that incorporated learning was completed in 1955 and demonstrated on television on February 24, 1956. Before describing his learning methods, I’ll describe in general how Samuel’s program chose moves. The technique is quite similar to how moves were chosen in the eight-puzzle I described earlier. Except now, provision must be made for the fact that the opponent chooses moves also. Again, a tree of symbolic expressions, representing board positions, is constructed. Starting with the initial configuration, all possible moves by the program (under the assumption that the program moves first) are considered. The result is all the possible resulting board configurations branching out from the starting configuration. Then, from each of these, all possible moves of the opponent are considered – resulting in more branches, and so on. If such a tree could be constructed for an entire game, a winning move could be computed by examination of the tree. Unfortunately, it has been estimated that there are about 5 × 1020 possible checkers positions. A leading expert in programming computers to play games, Jonathan Schaeffer, was able to “solve” checkers (showing that optimal play by both players results in a draw) by time-consuming analysis of around 1014 positions. He wrote me that “This was the result of numerous enhancements aimed at focussing the search at the parts of the search space where we were most likely to find what we 124 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 125. 5.4 Game-Playing Programs Figure 5.5: Arthur Samuel. (Photograph courtesy of Donna Hussain, Samuel’s daughter.) needed.”23 I’ll describe his work in more detail later. Samuel’s program then could necessarily construct only a part of the tree – that is, it could look only a few moves ahead. How far ahead it looked, along various of its branches, depended on a number of factors that need not concern us here. (They involved such matters as whether or not an immediate capture was possible.) Looking ahead about three moves was typical, although some branches might be explored (sparsely) to a depth of as many as ten moves. A diagram from Samuel’s paper, shown in Fig. 5.6, gives the general idea. Samuel said that the “actual branchings are much more numerous.” So, how is the program to choose a move from such an incomplete tree? This problem is faced by all game-playing programs, and they all use methods that involve computing a score for the positions at the tips, or “leaves,” of the tree (that is, the leaves of the incomplete tree generated by the program) and then “migrating” this score back up to the positions resulting from moves from the current position. First, I will describe how to compute the score, then how Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 125
- 126. 5 Early Heuristic Programs Figure 5.6: An illustrative checkers game tree. (From p. 74 of Edward A. Feigenbaum and Julian Feldman (eds.), Computers and Thought, New York: McGraw Hill, 1963.) to migrate it back, and then how Samuel used learning methods to improve performance. Samuel’s program first computed the points to be awarded to positions at the leaves of the tree based on their overall “goodness” from the point of view of the program. Among the features contributing points were the relative piece advantage (with kings being worth more than ordinary pieces), the overall “mobility” (freedom to move) of the program’s pieces, and center control. 126 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 127. 5.4 Game-Playing Programs (The program had access to 38 such features but only used the 16 best of these at any one time.) The points contributed by each feature were then multiplied by a “weight” (reflecting the relative importance of its corresponding feature), and the result was summed to give an overall score for a position. Starting with a position immediately above those at the tip of the tree, if it is a position for which it is the program’s turn to move, we can assume that the program would want to move to that position with the highest score, so that highest score is migrated back to this “immediately above” position. If, however, it is a position from which it is the opponent’s turn to move, we assume that the opponent would want to move to that position with the lowest score. In that case, the lowest score is migrated back to this immediately above position. This alternately “highest–lowest” migration strategy is continued back all the way up the tree and is called the “minimax” strategy. [A simple modification of this strategy, called the “alpha–beta” procedure, is used to infer (correctly) from already-migrated scores that certain branches need not be examined at all – thus allowing other branches to be explored more deeply. Opinions differ about who first thought of this important modification. McCarthy and Newell and Simon all claim credit. Samuel told me he used it but that it was too obvious to write about.] If one assumes that it is the program’s turn to move from the current position, and that scores have already been migrated back to the positions just below it, the program would make its move to that position with the highest score. And then the game would continue with the opponent making a move, another stage of tree growth, score computation and migration, and so on until one side wins or loses. One of the learning methods in Samuel’s program adjusted the values of the weights used by the scoring system. (Recall that weight adjustments in Pandemonium and in neural networks were ways in which those systems learned.) The weights were adjusted so that the score of a board position (as computed by the sum of the weighted feature scores) moved closer to the value of its migrated score after finishing a search. For example, if the score of an initial position was computed (using the weights before adjustment) to be 22, and the migrated score of that position after search was 30, then the weights used to compute the score of the initial position were adjusted in a manner so that the new score (using the adjusted value of the weights) was made closer to 30, say 27. (This technique foreshadowed a very important learning method later articulated by Richard Sutton called “temporal-difference learning.”) The idea here was that the migrated score, depending as it did on looking ahead in the game, was presumed to be a better estimate than the original score. The estimating procedure was thereby improved so that it produced values more consistent with the “look-ahead” score. Samuel also used another method called “rote learning” in which the program saved various board positions and their migrated scores encountered Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 127
- 128. 5 NOTES during actual play. Then, at the end of a search, if a leaf position encountered was the same as one of these stored positions, its score was already known (and would not have to be computed using the weights and features.) The known score, based as it was on a previous search, would presumably be a better indicator of position value than would be the computed score. Samuel’s program also benefitted from the use of “book games,” which are records of the games of master checkers players. In commenting about Samuel’s work, John McCarthy wrote that “checker players have many volumes of annotated games with the good moves distinguished from the bad ones. Samuel’s learning program used Lee’s Guide to Checkers24 to adjust its criteria for choosing moves so that the program would choose those thought good by checker experts as often as possible.” Samuel’s program played very good checkers and, in the summer of 1962, beat Robert Nealey, a blind checkers master from Connecticut. (You can see a game played between Mr. Nealey and Samuel’s program at http://www.fierz.ch/samuel.htm.) But, according to Jonathan Schaeffer and Robert Lake, “In 1965, the program played four games each against Walter Hellman and Derek Oldbury (then playing a match for the World Championship), and lost all eight games.”25 Notes 1. A. Newell and H. A. Simon, “The Logic Theory Machine: A Complex Information Processing System,” Proceedings IRE Transactions on Information Theory, Vol. IT-2, pp. 61–79, September 1956, and A. Newell, J. C. Shaw, and H. A. Simon, “Empirical Explorations of the Logic Theory Machine: A Case Study in Heuristics,” Proceedings of the 1957 Western Joint Computer Conference, Institute of Radio Engineers, pp. 218–230, 1957. [113] 2. Alfred North Whitehead and Bertrand Russell, Principia Mathematica, Vol. 1, Cambridge: Cambridge University Press, 1910. [113] 3. Allen Newell and Herbert A. Simon, “Computer Science as Empirical Inquiry: Symbols and Search,” Communications of the ACM, Vol. 19, No. 3, pp. 113–126, March 1976. [114] 4. Claude E. Shannon, “Programming a Computer for Playing Chess,” Philosophical Magazine, Ser. 7, Vol. 41, No. 314, March 1950. Text available online at http://www.pi.infn.it/∼carosi/chess/shannon.txt. (The paper was first presented in March 1950 at the National Institute for Radio Engineers Convention in New York.) [117] 5. Pamela McCorduck, Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence, p. 106, San Francisco: W. H. Freeman and Co., 1979. [117] 6. John McCarthy, “A Tough Nut for Theorem Provers,” Stanford Artificial Intelligence Project Memo No. 16, July 17, 1964; available online at http://www-formal.stanford.edu/jmc/toughnut.pdf. [117] 7. Saul Amarel, “On Representations of Problems of Reasoning About Actions,” in Donald Michie (ed.), Machine Intelligence 3, pp. 131–171, Edinburgh: Edinburgh University Press, 1968. [118] 128 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 129. 5.4 NOTES 8. Herbert Gelernter, “Realization of a Geometry-Theorem Proving Machine,” Proceedings of the International Conference on Information Processing”, pp. 273–282, Paris: UNESCO House, Munich: R. Oldenbourg, and London: Butterworths, 1960. Also in Edward A. Feigenbaum and Julian Feldman (eds.), Computers and Thought, pp. 134–152, New York: McGraw Hill, 1963. [118] 9. From http://www.math.niu.edu/∼rusin/known-math/99/minsky. [119] 10. Allen Newell, J. C. Shaw, and Herbert A. Simon, “Report on a General Problem-Solving Program,” Proceedings of the International Conference on Information Processing”, pp. 256–264, Paris: UNESCO House, Munich: R. Oldenbourg, and London: Butterworths, 1960. [121] 11. For more about GPS as a theory and explanation for human problem solving, see Allen Newell and Herbert Simon, “GPS, a Program That Simulates Human Thought,” in H. Billings (ed.), Lernende Automaten, pp. 109–124, Munich: R. Oldenbourg KG, 1961. Reprinted in Computers and Thought, pp. 279–293. [121] 12. I adapt an example from http://www.math.grinnell.edu/∼stone/events/scheme-workshop/gps.html. [121] 13. See George Ernst and Allen Newell, GPS: A Case Study in Generality and Problem Solving, New York: Academic Press, 1969. [122] 14. James R. Slagle, “A Heuristic Program That Solves Symbolic Integration Problems in Freshman Calculus,” Ph.D. dissertation, MIT, May 1961. For an article about SAINT, see James R. Slagle, “A Heuristic Program That Solves Symbolic Integration Problems in Freshman Calculus,” Journal of the ACM, Vol. 10, No. 4, pp. 507–520, October 1963. [123] 15. Allen Newell, J. Shaw, and Herbert Simon, “Chess-Playing Programs and the Problem of Complexity,” IBM Journal of Research and Development, Vol. 2, pp. 320–335, October 1958. [123] 16. Chapter 5 of Hal’s Legacy: 2001’s Computer as Dream and Reality, David G. Stork (ed.), Cambridge, MA: MIT Press, 1996. See the Web site at http://mitpress.mit.edu/e-books/Hal/chap5/five3.html. [123] 17. Andrew Hodges, “Alan Turing and the Turing Test,” in Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer, Robert Epstein, Gary Roberts, and Grace Beber (ed.), Dordrecht, The Netherlands: Kluwer, 2009. See A. M. Turing, “Proposed Electronic Calculator,” report for National Physical Laboratory, 1946, in A. M. Turing’s ACE Report of 1946 and Other Papers, B. E. Carpenter and R. W. Doran (eds.), Cambridge, MA: MIT Press, 1986. [123] 18. http://en.wikipedia.org/wiki/Alan Turing. [123] 19. Marvin Minsky (ed.), “Introduction,” Semantic Information Processing, p. 12, Cambridge, MA: MIT Press, 1968. [123] 20. From a Web retrospective at http://www-db.stanford.edu/pub/voy/museum/samuel.html. [124] 21. See Christopher Strachey, “Logical or Non-mathematical Programmes,” Proceedings of the 1952 ACM National Meeting (Toronto), pp. 46–49, 1952. [124] 22. Arthur L. Samuel, “Some Studies in Machine Learning Using the Game of Checkers,” IBM Journal of Research and Development, Vol. 3, No. 3, pp. 210–229, 1959. Reprinted in Edward A. Feigenbaum and Julian Feldman (eds.), Computers and Thought, p. 71, New York: McGraw Hill, 1963. [124] 23. E-mail of February 14, 2009. [125] 24. John W. Dawson, Lee’s Guide to the Game of Draughts, Revised Edition, London: E. Marlborough, 1947. [128] Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 129
- 130. 5 NOTES 25. Jonathan Schaeffer and Robert Lake, “Solving the Game of Checkers,” Games of No Chance, pp. 119–133, MSRI Publications, Vol. 29, 1996. (Available online at http://www.msri.org/communications/books/Book29/files/schaeffer.pdf.) [128] 130 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 131. 6.1 Chapter 6 Semantic Representations The computer programs I have described so far performed transformations on relatively simple symbol structures, which were all that were required for the mathematical problems, puzzles, and games that these programs dealt with. The main effort was in coming up with and using problem-specific heuristics (such as features to be used in computing the value of a checkers position, for example) to limit the number of transformations of these structures in searches for solutions. As Minsky put it, “The most central idea of the pre-1962 period was that of finding heuristic devices to control the breadth of a trial-and-error search.”1 In the early 1960s, several Ph.D. research projects, some performed under Minsky’s direction at MIT, began to employ more complex symbol structures in programs for performing various intellectual tasks. Because of their rich, articulated content of information about their problem topic, these structures were called semantic representations.2 As Minsky wrote, “Within the small domain in which each program operates, the performance [of these programs] is not too bad compared with some human activities. . . . But much more important than what these particular experiments achieve are the methods they use to achieve what they do, for each is a first trial of previously untested ideas.”3 I’ll describe some examples of these sorts of projects and the new methods that they employed. 6.1 Solving Geometric Analogy Problems Thomas G. Evans (1934– ) programmed a system that was able to perform well on some standard geometric analogy tests. It was apparently the largest program written up to that time in John McCarthy’s new programming language, LISP (which I’ll describe later). I quote from an article based on Evans’s 1963 dissertation, which presented this work:4 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 131
- 132. 6 Semantic Representations We shall be considering the solution by machine of so-called “geometric-analogy” intelligence-test questions. Each member of this class of problems consists of a set of labeled line drawings. The task to be performed can be described by the question: “Figure A is to Figure B as Figure C is to which of the following figures?” For example [in Fig. 6.1] it seems safe to say that most people would agree with the program we are about to describe, in choosing [number 4] as the desired answer. Figure 6.1: An analogy problem. He further noted that “problems of this type are widely regarded as requiring a high degree of intelligence for their solution and in fact are used as a touchstone of intelligence in some general intelligence tests used for college admission and other purposes.” So, again, AI research concentrated on mechanizing tasks requiring human intelligence. Evans’s program first transformed the diagrams presented to it so that they revealed how they were composed out of parts. He called these “articular” representations. Of the possibly several decompositions possible, the one chosen by the program depended on its “context.” (This choice is one example of a heuristic used by the program.) For example, the diagram could either be decomposed into 132 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 133. 6.1 Solving Geometric Analogy Problems and or into and But if the analogy problem contained another diagram (part of the context): then the first decomposition would be chosen. Evans represented diagrams and their parts as complex symbol structures consisting of rather elaborate combinations of lists and lists of lists whose elements indicated which parts were inside or outside (or above or below) which other parts, and so on. Those details need not concern us here, but they did allow Evans to specify “rules” for his program that could be used to show how one diagram could be transformed into another. The program was able to infer which combinations of these rules transformed Figure A of a given problem into Figure B. Then it could apply this transformation to Figure C. If one of the multiple-choice answers resulted, it would give that one as its answer. Otherwise, the program “weakened” the transformation just enough so that one of the answers was produced, and that would be the program’s answer. Evans summarized his results as follows: Allowing ourselves only [the parts of the program actually implemented], our estimate would be that of the 30 geometric-analogy problems on a typical edition of the ACE tests, [the program] can successfully solve at least 15 and possibly as Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 133
- 134. 6 Semantic Representations many as 20 problems. He notes that this level of performance compares favorably with the average high school student. 6.2 Storing Information and Answering Questions Another of Minsky’s Ph.D. students during the early 1960s, Bertram Raphael (1936– ), focused on the problem of “machine understanding.” In his dissertation,5 Raphael explained that a computer should be considered able to “understand” if it can converse intelligently, i.e., if it can remember what it is told, answer questions, and make responses which a human observer considers reasonable. Raphael wanted to be able to tell things to a computer and then ask it questions whose answers could be deduced from the things it had been told. (The telling and asking were to be accomplished by typing sentences and queries.) Here are some examples of the kinds of things he wanted to tell it: Every boy is a person. A finger is part of a hand. There are two hands on each person. John is a boy. Every hand has five fingers. Given this information, Raphael would want his system to be able to deduce the answer to the question “How many fingers does John have?” Because Raphael wanted his system to communicate with people, he wanted its input and output languages to be “reasonably close to natural English.” He recognized that “the linguistic problem of transforming natural language input into a usable form will have to be solved before we obtain a general semantic information retrieval system.” This “linguistic problem” is quite difficult and still not “solved” even though much progress has been made since the 1960s. Raphael used various “devices” (as he called them and which are not germane to our present discussion) to “bypass [the general problem of dealing with natural language] while still utilizing understandable English-like input and output.” 134 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 135. 6.2 Storing Information and Answering Questions The main problem that Raphael attacked was how to organize facts in the computer’s memory so that the relevant deductions could be made. As Raphael put it, “The most important prerequisite for the ability to ‘understand’ is a suitable internal representation, or model, for stored information. The model should be structured so that information relevant for question-answering is easily accessible.”6 Raphael called his system SIR, for Semantic Information Retrieval, (which he programmed in LISP). He used the word “semantic” because SIR modeled sentences in a way dependent on their meanings. The sentences that SIR could deal with involved “entities” (such as John, boy, hand, finger, and so on) and relations among these entities (such as “set-membership,” “part–whole,” “ownership,” “above,” “beside,” and other spatial relationships.) The model, then, had to have ways for representing entities and the relationships among them. Entities such as John and boy were represented by the LISP computer words JOHN and BOY, respectively. (Of course, the computer had no way of knowing that the computer word JOHN had anything to do with the person John. Raphael could have just as well represented John in the computer by X13F27 so long as he used that representation consistently for John. Using the computer word JOHN was a mnemonic convenience for the programmer – not for the computer!) When representing the fact that John is a boy, SIR would “link” a computer expression (SUPER-SET JOHN BOY) to the expression JOHN and link a computer expression (SUB-SET BOY JOHN) to the expression BOY. Thus, if SIR were asked to name a boy, it could reply “JOHN” by referring to BOY in its model, looking at its SUB-SET link and retrieving JOHN. (I have simplified the representations somewhat to get the main ideas across; SIR’s actual representations were a bit more complicated.) SIR could deal with dozens of different entities and relations among them. Every time it was told new information, it would add new entities and links as needed. It also had several mechanisms for making logical deductions and for doing simple arithmetic. The very structure of the model facilitated many of its deductions because, as Minsky pointed out in his discussion of Raphael’s thesis, “the direct predicate-links. . . almost physically chain together the immediate logical consequences of the given information.”7 SIR was also the first AI system to use the “exception principle” in reasoning. This principle is best explained by quoting directly from Raphael’s thesis: General information about “all the elements” of a set is considered to apply to particular elements only in the absence of more specific information about those elements. Thus it is not necessarily contradictory to learn that “mammals are land animals” and yet “a whale is a mammal which always lives in water.” In the program, this idea is implemented by always referring for desired information Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 135
- 136. 6 Semantic Representations to the property-list [that is, links] of the individual concerned before looking at the descriptions of sets to which the individual belongs. The justification for this departure from the no-exception principles of Aristotelian logic is that this precedence of specific facts over background knowledge seems to be the way people operate, and I wish the computer to communicate with people as naturally as possible. The present program does not experience the uncomfortable feeling people frequently get when they must face facts like “a whale is a mammal which lives in water although mammals as a rule live on land.” The exception principle was studied by AI researchers in much more detail later and led to what is called default reasoning and nonmonotonic logics, as we shall see. 6.3 Semantic Networks It is instructive to think of SIR’s representational scheme in terms of a network. The entities (such as JOHN and BOY) are the “nodes” of the network, and the relational links (such as SUB-SET) are the connections between nodes. SIR was an early version of what would become an important representational idea in artificial intelligence, namely, semantic networks. It was not the first, however. John Sowa, who has written extensively about semantic networks, claims that the “oldest known semantic network was drawn in the 3rd century AD by the Greek philosopher Porphyry in his commentary on Aristotle’s categories.”8 In 1961 Margaret Masterman (1910–1986), Director of the Cambridge Language Research Unit, used a semantic network in a translation system in which concepts were ordered in a hierarchy.9 M. Ross Quillian, a student of Herb Simon’s at the Carnegie Institute of Technology, was interested, along with Newell and Simon, in computational models of human mental processes, specifically memory organization. He developed a memory model consisting of a semantic network of nodes representing English words. The nodes were interconnected by what he called “associative links.” In Quillian’s words, “In the memory model, ingredients used to build up a concept are represented by the token nodes naming other concepts, while the configurational meaning of the concept is represented by the particular structure of interlinkages connecting those token nodes to each other.” Quillian goes on to write that “[t]he central question asked in this research has been: What constitutes a reasonable view of how semantic information is organized within a person’s memory? In other words: What 136 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 137. 6.3 Semantic Networks sort of representational format can permit the ‘meanings’ of words to be stored, so that humanlike use of these meanings is possible?”10 I can illustrate how Quillian’s network format represents meaning by using one of his examples. Consider the different meanings of the word “plant.” One such meaning is given by linking the node PLANT to other nodes, such as LIVE, LEAF, FOOD, AIR, WATER, and EARTH, through connections that represent that a plant (according to this meaning of the word) is alive, has leaves, and gets its food from air, water, and earth. Another meaning of “plant” links PLANT to other nodes, such as PEOPLE, PROCESS, and INDUSTRY, through connections that represent that a plant (according to this other meaning of the word) is an apparatus that uses people for engaging in a process used in industry. According to Quillian, the meaning of a term is represented by its place in the network and how it is connected to other terms. This same idea is used in dictionaries where the meaning of a word is given by mentioning the relationship of this word to other words. The meanings of those other words are, in turn, given by their relationships to yet other words. So we can think of a dictionary as being like a large semantic network of words linked to other words. By using this view, the full meaning of a concept can be quite extensive. As Quillian puts it, Suppose that a person were asked to state everything he knows about the concept “machine.” . . . This information will start off with the more “compelling” facts about machines, such as that they are usually man-made, involve moving parts, and so on, and will proceed “down” to less and less inclusive facts, such as the fact that typewriters are machines, and then eventually will get to much more remote information about machines, such as the fact that a typewriter has a stop which prevents its carriage from flying off each time it is returned. We are suggesting that this information can all usefully be viewed as part of the subject’s concept of “machine.” In what way is Quillian’s network a model of human memory organization? Quillian explored two capabilities of human memory modeled by his network. One was comparing and contrasting two different words. Quillian proposed that this be done by a process that came to be called “spreading activation.” Conceptually, one starts at the nodes representing the two words and gradually traverses the links emanating from them, “activating” the nodes along the way. This process continues until the two “waves” of activation intersect, thus producing a “path” between the two original nodes. Quillian proposed that the total “distance” along this path between the two words could be used as a measure of their similarity. The path can be used to produce an account comparing the two words. (Quillian’s program had mechanisms for expressing this account in simple English.) Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 137
- 138. 6 NOTES To use one of Quillian’s examples, suppose we wanted to compare the words “cry” and “comfort.” The spreading activations would intersect at the word “sad,” and the English account would express something like “to cry is to make a sad sound, and to comfort is to make something less sad.” Quillian was also interested in how the network could be used to “disambiguate” two possible uses of the same word. Consider, for example, the sentence “After the strike, the president sent him away.” The network can encode different meanings of the word “strike.” One such might involve a labor dispute, another might involve baseball, and yet another involve a raid by military aircraft. Which of these meanings is intended by the sentence? Presumably, activation proceeding outward from the word “president” would eventually reach concepts having to do with labor disputes before reaching concepts having to do with baseball or the military. Thus, the “labor dispute” meaning would be preferred because it is “closer,” given that the word “president” is in the sentence. In contrast, a different conclusion would be reached for the sentence “After the strike, the umpire sent him away.” Quillian’s model differs from some later semantic networks in that it does not have a predetermined hierarchy of superclasses and subclasses. As Quillian puts it, “every word is the patriarch of its own separate hierarchy when some search process starts with it. Similarly, every word lies at various places down within the hierarchies of (i.e., is an ingredient in) a great many other word concepts, when processing starts with them.” Notes 1. Marvin Minsky (ed.), “Introduction,” Semantic Information Processing, p. 9, Cambridge, MA: MIT Press, 1968. [131] 2. It might be argued that the diagram used by Gelernter’s geometry program was an earlier use of a semantic representation. [131] 3. Marvin Minsky, op. cit., p. 1. [131] 4. Thomas G. Evans, “A Program for the Solution of a Class of Geometric-Analogy Intelligence-Test Questions,” in Marvin L. Minsky, op. cit., p. 271. [131] 5. Bertram Raphael, “SIR: Semantic Information Retrieval,” in Marvin Minsky, op. cit., pp. 33–145. (This is a partial reprint of his 1964 Ph.D. dissertation.) [134] 6. Marvin Minsky, op. cit., p. 35. [135] 7. Marvin Minsky, op. cit., p. 17. [135] 8. From an article by John F. Sowa at http://www.jfsowa.com/pubs/semnet.htm. (This is a revised and extended version of one that was originally written for the Encyclopedia of Artificial Intelligence, edited by Stuart C. Shapiro, Wiley, 1987, second edition, 1992.) [136] 9. Margaret Masterman, “Semantic Message Detection for Machine Translation, Using an Interlingua,” in Proceedings of the 1961 International Conference on Machine Translation of Languages and Applied Language Analysis, pp. 438–475, London: Her Majesty’s Stationery Office, 1962. [136] 138 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 139. 6.3 NOTES 10. M. Ross Quillian, “Semantic Memory,” Ph.D. dissertation, Carnegie Institute of Technology (now Carnegie Mellon University), October 1966. (This work also appears as Report AFCRL-66-189 and is partially reprinted in M. Minsky (ed.), Semantic Information Processing, pp. 216–270, Cambridge, MA: MIT Press, 1968.) [137] Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 139
- 140. 6 NOTES 140 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 141. 7.1 Chapter 7 Natural Language Processing Beyond pattern recognition of individual alphanumeric characters, whether they be of fixed font or handwritten, lies the problem of understanding strings of characters that form words, sentences, or larger assemblages of text in a “natural” language, such as English. To distinguish languages such as English from the languages used by computers, the former are usually called “natural languages.” In artificial intelligence, “understanding” natural language input usually means either converting it to some kind of memory model (such as the one used by Raphael in his SIR system or the semantic network used by Quillian) or the evocation of some action appropriate to the input. Natural languages are spoken as well as written. And, because speech sounds are not as well segmented as are the characters printed on a page, speech understanding presents additional difficulties, which I’ll describe in a later chapter. The inverse of understanding natural language input is generating natural language output – both written and spoken. Translating from one language to another involves both understanding and generation. So does carrying on a conversation. All of these problems – understanding, generation, translation, and conversing – fall under the general heading of “natural language processing” (sometimes abbreviated as NLP). 7.1 Linguistic Levels Linguists and others who study language recognize several levels at which language can be analyzed. These levels can be arranged in a sort of hierarchy, starting with those dealing with the most basic components of language Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 141
- 142. 7 Natural Language Processing (sounds and word parts) and proceeding upward to levels dealing with sequences of sentences. If speech is being dealt with, there are the levels of phonetics (language sounds) and phonology (organization of sounds into words). For both speech and text, morphology deals with how whole words are put together from smaller parts. For example, “walking” consists of “walk” plus “-ing.” Next, syntax is concerned with sentence structure and grammar. It attempts to describe rules by which a string of words in a certain language can be labeled either grammatical or not. For example, the string “John hit the ball” is grammatical but the string “ball the hit John” is not. Together with the dictionary definitions of words, syntax comes next in importance for understanding the meaning of a sentence. For example, the sentence “John saw the man with a telescope” has two different meanings depending on its syntactic structure (that is, depending on whether “with a telescope” refers to “the man” who had a telescope or to “saw.”) But grammaticality alone is insufficient for determining meaning. For example, the sentence “Colorless green ideas sleep furiously” might be considered grammatical, but it is nonsensical. The semantics level helps to determine the meaning (or the meaninglessness) of a sentence by employing logical analyses. For example, through semantic analysis, an “idea” can’t be both “colorless” and “green.” Next comes the pragmatics level, which considers the context of a sentence to pin down meaning. For example, “John went to the bank” would have a different meaning in a sentence about stream fishing than it would in a sentence about commerce. Pragmatics deals with meanings in the context of specific situations. One of these levels in particular, namely, syntax, was the subject of much early study and continues to be an important aspect of NLP. In 1957, the American linguist Noam Chomsky published a ground-breaking book titled Syntactic Structures in which he proposed sets of grammatical rules that could be used for generating the “legal” sentences of a language.1 The same rules could also be used to analyze a string of words to determine whether or not they formed a legal sentence of the language. I’ll illustrate how this analysis is done using what Chomsky called a phrase-structure grammar (PSG).2 The process is very similar to how we all “diagrammed” sentences back in grade school. Grammars are defined by stating rules for replacing words in the string by symbols corresponding to syntactic categories, such as noun or verb or adjective. Grammars also have rules for replacing strings of these syntactic symbols by additional symbols. To illustrate these ideas, I’ll use a very simple grammar adapted from one of Chomsky’s examples. This grammar has only three syntactic categories: determiner, noun, and verb. Those three are sufficient for analysing strings such as “the man hit the ball.” 142 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 143. 7.1 Linguistic Levels One of the rules in this illustrative grammar states that we can replace either of the words “the” or “a” by the symbol “DET” (for determiner). Linguists write this rule as follows: the | a → DET (The symbol | is used to indicate that either of the words that surround it can be replaced by the syntactic symbol to the right of the arrow.) Here are some other rules, written in the same format: man | ball | john → N (The words “man,” “ball,” and “john” can be replaced by the symbol “N” for noun.) hit | took | threw → V (The words “hit,” “took,” and “threw” can be replaced by the symbol “V” for verb.) DET N → NP (The string of symbols “DET” and “N” can be replaced by the symbol “NP” for noun phrase.) V NP → VP (The string of symbols “V” and “NP” can be replaced by the symbol “VP” for verb phrase.) NP VP → S (The string of symbols “NP” and “VP” can be replaced by the symbol “S” for sentence.) Symbols such as “S,” “DET,” “NP,” and so on are called the “nonterminal” symbols of the language defined by the grammar, whereas vocabulary words such as “ball,” “john,” and “threw” are the “terminal” symbols of the language. We can apply these rules to the string “the man hit the ball” to transform it into “S.” Any string that can be changed into “S” in this way is said to be grammatical – a legal sentence in the language defined by this very simple grammar. One way to illustrate the rule applications, called a parse tree, is shown in Fig. 7.1.3 This example was based on a small set of syntactic categories and replacement rules just to illustrate the main ideas about syntactic analysis. To make the grammar slightly more realistic, we would need to include symbols and replacement rules for adjectives, adverbs, prepositions, and so on. And, of course, we would have to include many more vocabulary words. Grammars are called context-free grammars (CFGs) if all of their rules have just a single nonterminal symbol on the right side of the arrow. They are Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 143
- 144. 7 Natural Language Processing Figure 7.1: A parse tree for analyzing a sentence. called that because when the rules are used in reverse (to generate rather than to analyze grammatical sentences), the way in which a nonterminal symbol is replaced does not depend on the presence of any other symbols. PSGs are context free. The diagram in Fig. 7.2 shows how the rules of our simple grammar can be used to generate sentences. In this case, it starts with the symbol for sentence, namely, “S,” and generates the sentence “John threw the ball.” This simple grammar certainly can’t generate all of the sentences we would claim to be legal or acceptable. It also generates sentences that we would not ordinarily want to accept, such as “the john threw the ball.” Chomsky’s book presents much more complex grammars, and later work has 144 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 145. 7.1 Linguistic Levels Figure 7.2: A parse tree for generating a sentence. produced quite elaborate ones. By the early 1960s, several grammars had been encoded in computer programs that could parse samples of English text.4 I’ll be mentioning several different grammars, some more complex than CFGs in succeeding chapters. Nevertheless, even the most complex grammars can’t cleanly distinguish between sentences we would accept as grammatically correct and those we would not. I will return to this difficulty and one way to deal with it in a later chapter. The way a sentence is parsed by a grammar can determine its meaning, so Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 145
- 146. 7 Natural Language Processing an important part of natural language processing involves using the grammar rules to find acceptable parse trees for sentences. Finding a parse tree involves search – either for the several different ways that the nonterminal symbols, beginning with “S,” can be replaced using grammar rules in an attempt to match a target sentence or for the several different ways the words in a target sentence can be replaced by nonterminal symbols in an attempt to produce the symbol “S.” The first of these kinds of searches is called “top-down” (from “S” to a sentence); the second is called “bottom-up” (from a sentence to “S”). It is often (if not usually) the case that, given a grammar, sentences can have more than one parse tree, each with a different meaning. For example, “the man hit the ball in the park” could have a parse tree in which “in the park” is part of a verb phrase along with “hit” or a parse tree in which “in the park” is part of a noun phrase along with “ball.” Moreover, as I have already mentioned, some parsings of sentences might be meaningless. For example, according to my simple grammar, “the ball threw the man” is a legal but probably meaningless sentence. Deciding which parse tree is appropriate is part of the process of deciding on meaning and is a job for the semantics (and possibly even the pragmatics) level. During the late 1950s and throughout most of the 1960s and beyond, syntactic analysis was more highly developed than was semantics. Semantic analysis usually involves using the parse tree to guide the transformation of the input sentence into an expression in some well-defined “meaning representation language” or into a program that responds in the appropriate way to the input sentence. For example, “the man threw the ball” might be transformed into a logical expression such as (∃x, y, z)[Past(z) ∧ Man(x, z) ∧ Ball(y, z) ∧ Event(z) ∧ Throws(x, y, z)], which can be interpreted as “there are x, y, and z, such that z is an event that occurred in the past, x is a man in that event, y is a ball in that event, and x throws y in that event.” Semantic analysis might also transform the sentence “the man threw the ball” into a program that, in some way, simulates a man throwing the ball in the past. 7.2 Machine Translation Some of the first attempts to use computers for more than the usual numerical calculations were in automatic translation of sentences in one language into sentences of another. Word dictionaries could be stored in computer memory (either on tapes or on punched cards), and these could be used to find English equivalents for foreign words. It was thought that selecting an appropriate equivalent for each foreign word in a sentence, together with a modest amount 146 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 147. 7.2 Machine Translation of syntactic analysis, could be used to translate a sentence in a foreign language (Russian, for example) into English. Reporting about a new computer5 being developed by a team led by Harry D. Huskey at the National Bureau of Standards (now called the National Institute of Standards and Technology), the New York Times reported the following on May 31, 1949:6 A new type of “electric brain” calculating machine capable not only of performing complex mathematical problems but even of translating foreign languages, is under construction here at the United States Bureau of Standards Laboratory at the University of California’s Institute of Numerical Analysis. While the exact scope the machine will have in the translating field has not been decided, the scientists working on it say it would be quite possible to make it encompass the 60,000 words of the Webster Collegiate Dictionary with equivalents for each word in as many as three foreign languages. Explaining how the machine might do translation, the Times reporter wrote When a foreign word for translation is fed into the machine, in the form of an electro-mathematical symbol on a tape or card, the machine will run through its “memory” and if it finds that symbol as record, will automatically emit a predetermined equivalent – the English word. . . . This admittedly will amount to a crude word-for-word translation, lacking syntax, but will nevertheless be extremely valuable, the designers say, for such purposes as scientists’ translations of foreign technical papers in which vocabulary is far more of a problem than syntax. The machine had not actually performed any translations – the idea of doing so was still just a possibility envisioned by Huskey. But even nonscientists could imagine the difficulties. An editorial in the New York Times the next day put the problem well: We have our misgivings about the accuracy of every translation. How is the machine to decide if the French word “pont” is to be translated as “bridge” or “deck” or to know that “operation” in German means a surgical operation? All the machine can do is to simplify the task of looking up words in a dictionary and setting down their English equivalents on a tape, so that the translator Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 147
- 148. 7 Natural Language Processing still has to frame the proper sentences and give the words their contextual meaning. In a 1947 letter to Norbert Wiener, Warren Weaver, a mathematician and science administrator, mentioned the possibility of using digital computers to translate documents between natural human languages. Wiener was doubtful about this possibility. In his reply to Weaver, Wiener wrote “I frankly am afraid the boundaries of words in different languages are too vague and the emotional and international connotations are too extensive to make any quasi-mechanical translation scheme very hopeful.” Nevertheless, by July 1949, Weaver had elaborated his ideas into a memorandum, titled “Translation” that he sent to several colleagues. Weaver began his memorandum by stating the following: There is no need to do more than mention the obvious fact that a multiplicity of languages impedes cultural interchange between the peoples of the earth, and is a serious deterrent to international understanding. The present memorandum, assuming the validity and importance of this fact, contains some comments and suggestions bearing on the possibility of contributing at least something to the solution of the world-wide translation problem through the use of electronic computers of great capacity, flexibility, and speed. According to the editors of the published volume7 in which the memorandum was reprinted, “When he sent it to some 200 of his acquaintances in various fields, it was literally the first suggestion that most had ever seen that language translation by computer techniques might be possible.” Weaver’s document is often credited with initiating the field of machine translation (often abbreviated as MT).8 In June 1952 at MIT, Yehoshua Bar-Hillel (1915–1975), an Israeli logician who was then at MIT’s Research Laboratory for Electronics, organized the first conference devoted to machine translation.9 Originally optimistic about the possibilities, Bar-Hillel was later to conclude that full automatic translation was impossible. In January 1954, automatic translation of samples of Russian text to English was demonstrated at IBM World Headquarters, 57th Street and Madison Avenue, New York City. The demonstration, using a small vocabulary and limited grammar, was the result of a collaboration between IBM and Georgetown University. The project was headed by Cuthbert Hurd, director of the Applied Sciences Division at IBM, and Léon Dostert of Georgetown. According to an IBM press release10 on January 8, 1954, Russian was translated into English by an electronic “brain” today for the first time. 148 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 149. 7.2 Machine Translation Brief statements about politics, law, mathematics, chemistry, metallurgy, communications and military affairs were submitted in Russian by linguists of the Georgetown University Institute of Languages and Linguistics to the famous 701 computer of the International Business Machines Corporation. And the giant computer, within a few seconds, turned the sentences into easily readable English. A girl who didn’t understand a word of the language of the Soviets punched out the Russian messages on IBM cards. The “brain” dashed off its English translations on an automatic printer at the breakneck speed of two and a half lines per second. “Mi pyeryedayem mislyi posryedstvom ryechyi,” the girl punched. And the 701 responded: “We transmit thoughts by means of speech.” “Vyelyichyina ugla opryedyelyayetsya otnoshyenyiyem dlyini dugi k radyiusu,” the punch rattled. The “brain” came back: “Magnitude of angle is determined by the relation of length of arc to radius.” Although the demonstration caused a great deal of excitement and led to increased funding for translation research, subsequent work in the field was disappointing.11 Evaluating MT work in a 1959 report circulated among researchers, Bar-Hillel had become convinced that fully automatic, high-quality translation (which he dubbed FAHQT) was not feasible “not only in the near future but altogether.” His expanded report appeared in a 1960 paper that enjoyed wide distribution.12 One of the factors leading Bar-Hillel to his negative conclusions was the apparent difficulty of giving computers the “world knowledge” they would need for high-quality translation. He illustrated the problem with the following story: Little John was looking for his toy box. Finally he found it. The box was in the pen. John was very happy. How should one translate “The box was in the pen”? Bar-Hillel argued that even if there were only two definitions of “pen” (a writing utensil and an enclosure where small children play), a computer knowing only those definitions would have no way of deciding which meaning was intended. In addition to its knowledge of vocabulary and syntax, a translating computer would need to know “the relative sizes of pens, in the sense of writing implements, toy boxes, and pens, in the sense of playpens.” Such knowledge, Bar-Hillel claimed, was not at the disposal of the electronic computer. He said that giving a computer such encyclopedic knowledge was “utterly chimerical and hardly deserves any further discussion.” Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 149
- 150. 7 Natural Language Processing As later researchers would finally concede, Bar-Hillel was right about his claim that highly competent natural language processing systems (indeed, broadly competent AI systems in general) would need to have encyclopedic knowledge. However, most AI researchers would disagree with him about the futility of attempting to give computers the required encyclopedic knowledge. Bar-Hillel was well known for being a bit of a nay-sayer regarding artificial intelligence. (Commenting on John McCarthy’s “Programs with Common Sense” paper at the 1958 Teddington Conference, Bar-Hillel said “Dr. McCarthy’s paper belongs in the Journal of Half-Baked Ideas, the creation of which was recently proposed by Dr. I. J. Good.”)13 In April 1964, the National Academy of Sciences formed the Automatic Language Processing Advisory Committee (ALPAC), with John R. Pierce (1910–2002) of Bell Laboratories as chair, to “advise the Department of Defense, the Central Intelligence Agency, and the National Science Foundation on research and development in the general field of mechanical translation of foreign languages.” The committee issued its report in August 1965 and concluded, among other things, that “. . . there is no immediate or predictable prospect of useful machine translation.”14 They recommended support for basic linguistics science and for “aids” to translation, but not for further support of fully automatic translation. This report caused a dramatic reduction of large-scale funding of research on machine translation. Nonetheless, machine translation survived and eventually thrived, as we shall see in later chapters. The Association for Machine Translation and Computational Linguistics (AMTCL) held its first meeting in 1962. In 1968, it changed its name to the Association for Computational Linguistics (ACL) and has become an international scientific and professional society for people working on problems involving natural language and computation. It publishes the quarterly journal Computational Linguistics and sponsors conferences and workshops.15 7.3 Question Answering In addition to work on machine translation, researchers began exploring how sentences in a natural language, such as English, could be used to communicate with computers. You will recall Weizenbaum’s ELIZA program that was able to engage a person in a conversation even though the program “understood” nothing about what was being said. And, I have already mentioned Raphael’s SIR system that could represent information given to it and then answer questions. I’ll mention a few other projects to give a flavor of natural language processing work during this period. A program called BASEBALL (written in IPL-V, a special list-processing programming language developed by Newell, Shaw, and Simon to be described later) was developed at the Lincoln 150 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 151. 7.3 Question Answering Laboratory under the direction of Bert Green, a professor of Psychology at the Carnegie Institute of Technology.16 It could answer simple English questions about baseball using a database about baseball games played in the American League during a single year. For example, it could answer a question such as “Where did the Red Sox play on July 7?” The questions had to be of a particularly simple form and restricted to words in the program’s vocabulary. In the authors’ words,17 Questions are limited to a single clause; by prohibiting structures with dependent clauses the syntactic analysis is considerably simplified. Logical connectives, such as and, or, and not, are prohibited, as are constructions implying relations like most and highest. Finally, questions involving sequential facts, such as “Did the Red Sox ever win six games in a row?” are prohibited. The program worked by converting a question into a special form called a “specification list” using both special-purpose syntactic and semantic analyses. This list would then be used to access the program’s database to find an answer to the question. For example, the question “Where did the Red Sox play on July 7?” would first be converted to the list: Place = ? Team = Red Sox Month = July Day = 7 The authors claimed that their “restrictions were temporary expedients that will be removed in later versions of the program.” As far as I know, there were no later versions of the program. (As we will see as my history of AI unfolds, there are several instances in which it proved very difficult to remove “temporary” restrictions.) Another natural language program, SAD SAM, was written in IPL-V in 1962–1963 by Robert Lindsay at the Carnegie Institute of Technology.18 It could analyze English sentences about family relationships and encode these relationships in a family tree. Using the tree, it could then answer English questions about relationships. For example, if SAD SAM received the sentence “Joe and Jane are Tom’s offspring,” it would construct a treelike list structure for a certain “family unit” in which Tom is the father and Joe and Jane are the children. Then, if it received the sentence “Mary is Jane’s mother,” it would add Mary to this structure as Tom’s wife. It would then be able to answer the question “Who is Joe’s mother?” SAD SAM is an acronym for Sentence Appraiser and Diagrammer and Semantic Analyzing Machine. The SAD part parsed the input sentences and Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 151
- 152. 7 Natural Language Processing passed them to SAM, which extracted the semantic information needed for building family trees and for finding answers to questions. The program could accept a wide variety of sentences in Basic English – a system of grammar and a vocabulary of about 850 words defined by Charles K. Ogden.19 Robert F. Simmons (1925–1994), a psychologist and linguist at the Systems Development Corporation (SDC) in Santa Monica, California, had grander goals for his own work in natural language processing. According to an “In Memoriam” page written by Gordon Novak, one of his Ph.D. students at the University of Texas in Austin where Simmons took up a position as Professor of Computer Sciences and Psychology,20 Simmons’ dream was that one could have “a conversation with a book;” the computer would read the book, and then the user could have a conversation with the computer, asking questions to be answered from the computer’s understanding of the book. Accomplishing this “dream” would turn out to be as hard as AI itself. In a 1961 note about his proposed “Synthex” project, Simmons described how he would begin:21 The objective of this project is to develop a research methodology and a vehicle for the design and construction of a general purpose computerized system for synthesizing complex human cognitive functions. The initial vehicle, proto-synthex, will be an elementary language-processing device which reads simple printed material and answers simple questions phrased in elementary English. By 1965, Simmons and Lauren Doyle had conducted some experiments with their Protosynthex system. According to a report by Trudi Bellardo Hahn,22 “A small prototype full-text database of chapters from a child’s encyclopedia (Golden Book) was loaded on the system. Protosynthex could respond to simple questions in English with an ‘answer.’ . . . it was a pioneering effort in the use of natural language for text retrieval.” In the meantime, Daniel G. Bobrow (1935– ), a Ph.D. student of Marvin Minsky’s at MIT, wrote a set of programs, called the STUDENT system, that could solve algebra “story problems” given to it in a restricted subset of English. Here is an example of a problem STUDENT could solve: The distance from New York to Los Angeles is 3000 miles. If the average speed of a jet plane is 600 miles per hour, find the time it takes to travel from New York to Los Angeles by jet. STUDENT solved the problem by using some known relationships about speed and distance to set up and solve the appropriate equations. Bobrow’s dissertation gave several other examples of problems STUDENT could solve and the methods used.23 152 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 153. 7.3 NOTES Notes 1. Noam Chomsky, Syntactic Structures, ’s-Gravenhage: Mouton & Co., 1957. [142] 2. The basic structure of PSGs was independently invented by computer scientist John Backus to describe the syntax of the ALGOL programming language. See John Backus, “The Syntax and Semantics of the Proposed International Algebraic Language of the Zürich ACM-GAMM Conference,” Proceedings on the International Conference on Information Processing, pp. 125–132, UNESCO, 1959. [142] 3. According to C. George Boeree (see http://www.ship.edu/∼cgboeree/wundtjames.html), Wilhelm Wundt “invented the tree diagram of syntax we are all familiar with in linguistics texts.” [143] 4. For a survey of work during this period, see Daniel Bobrow, “Syntactic Analysis of English by Computer: A Survey,” Proceedings of the 1963 Fall Joint Computer Conference, Vol. 24, pp. 365–387, Baltimore: Spartan Books, 1963. [145] 5. The Standards Western Automatic Computer (later abbreviated to SWAC) [147] 6. The quotation appears in John Hutchins, “From First Conception to First Demonstration: The Nascent Years of Machine Translation, 1947–1954. A chronology,” Machine Translation, Vol. 12 No. 3, pp. 195–252, 1997. (A corrected 2005 version, with minor additions, appears at http://www.hutchinsweb.me.uk/MTJ-1997-corr.pdf.) [147] 7. W. N. Locke and A. D. Booth (eds.), Machine Translation of Languages: Fourteen Essays, pp. 15–23, Cambridge, MA: MIT Press, 1955. [148] 8. For a history of MT, see W. John Hutchins, “Machine Translation: A Brief History,” in E. F. K. Koerner and R. E. Asher (eds.), Concise History of the Language Sciences: From the Sumerians to the Cognitivists, pp. 431–445, Oxford: Pergamon Press, 1995. (Also available online at http://www.hutchinsweb.me.uk/ConcHistoryLangSci-1995.pdf.) Hutchins also has a Web page devoted to his publications at http://ourworld.compuserve.com/homepages/WJHutchins/#History1. [148] 9. For reports about this conference see E. Reifler, “The First Conference on Mechanical Translation,” Mechanical Translation, Vol. 1 No. 2, pp. 23–32, 1954, and A. C. Reynolds, “The Conference on Mechanical Translation Held at MIT, June 17–20, 1952,” Mechanical Translation, Vol. 1, No. 3, pp. 47–55, 1954. [148] 10. http://www-03.ibm.com/ibm/history/exhibits/701/701 translator.html. [148] 11. For a summary of the IBM–Georgetown work, see W. John Hutchins, “The Georgetown–IBM Experiment Demonstrated in January 1954,” in Robert E. Frederking and Kathryn B.Taylor (eds.), Proceedings of Machine Translation: From Real Users to Research, 6th Conference of the Association for Machine Translation in the Americas, AMTA-2004, pp. 102–114, Washington DC, USA, September 28–October 2, 2004, Berlin: Springer, 2004. An online version is available at http://www.hutchinsweb.me.uk/ATMA-2004.pdf. [149] 12. Yehoshua Bar-Hillel, “The Present Status of Automatic Translation of Languages,” Advances in Computers, Vol. 1, No. 1, pp. 91–163, 1960. [149] 13. In D. V. Blake and A. M. Uttley (eds.), Proceedings of the Symposium on Mechanisation of Thought Processes, p. 85, London: Her Majesty’s Stationary Office, 1959. [150] 14. John R. Pierce et al., Language and Machines: Computers in Translation and Linguistics, ALPAC Report, National Academy of Sciences Publication 416, National Research Council, Washington, DC, 1966. [150] 15. See http://www.aclweb.org/. [150] 16. Bert F. Green Jr., Alice K. Wolf, Carol Chomsky, and Kenneth Laughery, “BASEBALL: An Automatic Question Answerer,” pp. 219–224, Proceedings of the Western Joint Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 153
- 154. 7 NOTES Computer Conference, May 1961. Reprinted in Edward A. Feigenbaum and Julian Feldman (eds.), Computers and Thought, pp. 207–216, New York: McGraw Hill, 1963, and in B. Grosz, K. Spark Jones, and B. Lynn Webber (eds.), Readings in Natural Language Processing, Morgan Kaufman, Los Altos, CA, 1986. [151] 17. Ibid. [151] 18. See Robert K. Lindsay, “Inferential Memory as the Basis of Machines Which Understand Natural Language,” in Edward A. Feigenbaum, and Julian Feldman, op. cit., pp. 217–233. [151] 19. Charles K. Ogden, Basic English: A General Introduction with Rules and Grammar, 4th edition, London: Kegan, Paul, Trench, Trubner & Co., Ltd., 1933. (Lindsay says 1,700 words; other sources say 850.) [152] 20. From http://www.cs.utexas.edu/users/ai-lab/simmons.html. [152] 21. Robert F. Simmons, “Synthex,” Communications of the ACM, Vol. 4 , No. 3, p. 140, March 1961. [152] 22. From “Text Retrieval Online: Historical Perspective on Web Search Engines,” by Trudi Bellardo Hahn, ASIS Bulletin, April/May 1998. Available online at http://www.asis.org/Bulletin/Apr-98/hahn.html. [152] 23. Daniel G. Bobrow, “Natural Language Input for a Computer Problem Solving System,” MIT Artificial Intelligence Project Memo 66, Memorandum MAC-M-148, March 30, 1964. Available online at http://dspace.mit.edu/bitstream/handle/1721.1/5922/AIM-066.pdf?sequence=2. An article based on the dissertation is Chapter 3 of Marvin Minsky (ed.), Semantic Information Processing, Cambridge, MA: MIT Press, 1968. [152] 154 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 155. 8.1 Chapter 8 1960s’ Infrastructure The technical developments during the 1960s were aided (indeed, one might say made possible) by several systems support and societal factors. New computer languages made it much easier to build AI systems. Researchers from mathematics, from cognitive science, from linguistics, and from what soon would be called “computer science” came together in meetings and in newly formed laboratories to attack the problem of mechanizing intelligent behavior. In addition, government agencies and companies, concluding that they had an important stake in this new enterprise, provided needed research support. 8.1 Programming Languages Newell and Simon were among the first to realize that a specialized computer language would be useful for manipulating the symbolic expressions that were at the heart of their approach to mechanizing intelligence. The most elementary kind of symbolic expression is a list of symbols, such as (7, B, 5). More complex structures can be composed by creating lists of lists of symbols and lists of lists of lists, and so on. In my description of symbol structures for the eight-puzzle, I mentioned the kinds of manipulations that are needed. Recall that the starting position of the eight-puzzle was represented by the expression ((2, 8, 3), (1, 6, 4), (7, B, 5)). What was needed was a language for writing programs that could produce expressions representing the positions corresponding to moves of the puzzle. For example, one of the moves that can be made from the starting position is represented by the expression ((2, 8, 3), (1, 6, 4), (B, 7, 5)). Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 155
- 156. 8 1960s’ Infrastructure To produce this expression, the program must copy the starting position expression and then interchange the first and second elements of the third list in that expression. Newell, Shaw, and Simon set about to develop a language in which these kinds of manipulations could be programmed. Starting around 1954 at the RAND Corporation, they created a series of languages all called IPL (for information-processing language). Several versions of the language were developed. IPL-I was not actually implemented but served as a design specification. IPL-II was implemented in 1955 for the RAND Corporation’s JOHNNIAC computer. Later versions (through IPL-VI) were implemented at Carnegie Tech. The IPL languages were used to program several early AI programs, including LT, GPS, NSS (the Newell, Shaw, Simon chess-playing program), and the programs written by Newell’s and Simon’s students, such as Quillian and George Ernst. After the Dartmouth summer project, John McCarthy also began thinking about using list-processing languages. He was aware of the use of FLPL (FORTRAN fortified by some list-processing operations) in Gelernter’s geometry theorem-proving machine. Ultimately, however, McCarthy concluded a new language was needed that was easier to use than IPL and more powerful than FLPL. Starting in the fall of 1958 at MIT, McCarthy began the implementation of a programming language he called LISP (for list processing). He based it (loosely) on a branch of mathematics of special interest in computation called recursive function theory. LISP had several elementary operations for copying a list, stripping off elements of a list, adding an element to a list, and checking to see whether something were an element of a list. From these, arbitrarily complex manipulations of lists could be composed. An important feature of LISP was that programs for manipulating lists were themselves represented as lists. Such programs could thus be elements of other lists and could have subprograms embedded in them. A program could even have a version of itself embedded in it. As I have already mentioned, programs that can activate versions of themselves as part of their operation are called “recursive” and are very useful (if used with the care needed to avoid endless circularity).1 Because it was easier to use, LISP soon replaced IPL as the primary language of artificial intelligence research and applications. The programs produced by Minsky’s students, Evans, Raphael, Bobrow, Slagle, and others, were all written in LISP. (Interestingly, Arthur Samuel did not use a list-processing language for writing his checkers-playing programs. Rather heroically, he programmed them in the base language of elementary machine operations to make them run efficiently and use memory sparingly.) Besides developing LISP, McCarthy proposed a method, called “time-sharing,” by which a single computer could be made to serve several users simultaneously – acting as if each user had his or her own private 156 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 157. 8.2 Early AI Laboratories machine.2 Working initially with Ed Fredkin at Bolt, Beranek, and Newman (BBN) and later with others, McCarthy developed an early time-sharing system at MIT using a DEC PDP-1 computer.3 8.2 Early AI Laboratories In 1955, Newell moved from the RAND Corporation to Carnegie Tech (which became Carnegie Mellon University, CMU, in 1967) to work on a Ph.D. degree in industrial management under Herb Simon. After completing his degree, Newell stayed on as a professor at Carnegie, and he and Simon began advising a number of Ph.D. students – using the phrase “complex information processing (CIP)” to describe their work. (For several years they avoided the AI sobriquet.) In the fall of 1956, Herb Simon took delivery of an IBM 650, which was the first computer used for CIP work. Later, they used an IBM 704, followed by a series of DEC machines. John McCarthy moved from Dartmouth to MIT in the fall of 1958. Minsky joined MIT a year later. As Minsky puts it,4 [McCarthy and I] were walking down the hall and we met Jerry Wiesner or Zimmerman or someone and he said how’s it going and we said well, we’re working on these artificial intelligence ideas but we need a little more room and support for some graduate students. So then a room appeared a few days later. . . The “room” soon developed into the MIT Artificial Intelligence Project. Initially, the group used MIT’s IBM 704 computer, which proved not to have sufficient memory for the programs being written. So it began to use a DEC PDP-1 belonging to BBN. With funding from another project at MIT, it bought its own PDP-1, which was followed by the PDP-6 and PDP-10. Several of the group’s Ph.D. students did their work at BBN and at the nearby Lincoln Laboratory where Oliver Selfridge continued his AI research – mainly on pattern recognition and machine learning. In 1962, McCarthy moved to Stanford where he began an AI project. Seymour Papert (1928– ), a mathematician who had worked with Jean Piaget, joined Minsky as co-director of the AI Lab in 1963. By 1965 at Stanford, McCarthy and colleagues had created a time-sharing system, called Thor, on a PDP-1 computer. It included twelve Philco display terminals, which made it the first display-oriented time-sharing system anywhere in the world. With the help of Lester Earnest (1930– ), who had moved to Stanford from Lincoln Laboratory, McCarthy set up the Stanford AI Laboratory (SAIL) in 1965. Outgrowing its on-campus facilities, SAIL moved to a building in the Stanford foothills during the summer of 1966. (See Fig. 8.1.) With additional Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 157
- 158. 8 1960s’ Infrastructure support from ARPA, the Lab took delivery of a DEC PDP-6 computer and, later, a PDP-10 computer. In addition to its work in AI (which I’ll describe in subsequent chapters), SAIL was involved in many other computer-related projects including the development of a precursor to computer “windows” and the early installation of terminals in everyone’s offices.5 Figure 8.1: Site of the Stanford AI Lab from 1966 until 1980. (Photograph courtesy of Lester Earnest.) Since their early days, the groups at CMU, MIT, and Stanford have been among the leaders of research in AI. Often graduates of one of these institutions became faculty members of one of the other ones. Around 1965 another world-class AI center emerged at the University of Edinburgh in Scotland. Its founder was Donald Michie (1923–2007; Fig. 8.2), who had worked with Alan Turing and I. J. (Jack) Good at Bletchley Park during the Second World War. Discussions there with Turing and Good about intelligent machines captivated Michie. As he reported in an October 2002 interview, “I resolved to make machine intelligence my life as soon as such an enterprise became feasible.”6 Because computer facilities in the mid- to late 1940s were primitive and scarce, Michie became a geneticist and molecular biologist. Pursuing his interest in machine intelligence, from the sidelines as it were, in 1960 he put together a “contraption of matchboxes and glass beads” that could learn to play tic-tac-toe (noughts and crosses). He named his “machine” MENACE, an acronym for Matchbox Educable Noughts and Crosses Engine.7 158 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 159. 8.2 Early AI Laboratories Figure 8.2: Donald Michie. (Photograph courtesy of the Michie Family.) (See Fig. 8.3.) (As I’ll explain later, MENACE foreshadowed work in what is now called “reinforcement learning.”) During a year-long visit to Stanford (sponsored by the Office of Naval Research) in the early 1960s, Michie met John McCarthy, Bernard Widrow, and others working in AI (including me). While there, he worked on a learning program for balancing a pole on a motor-driven cart. Figure 8.3: Michie’s MENACE for learning how to play tic-tac-toe. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 159
- 160. 8 1960s’ Infrastructure In January 1965, Michie became the Director of the UK’s first AI laboratory, the Experimental Programming Unit, at the University of Edinburgh. This group was to become the Department of Machine Intelligence and Perception in October 1966. Michie recruited some top-flight computer talent, including Rod Burstall, Robin Popplestone, and John Collins. Those three developed a list-processing language called POP-2, which was the language used for AI program-writing by members of the Unit. (I’ll describe some of these programs later.) For many years, Michie’s group worked collaboratively with a nearby University of Edinburgh group, the Metamathematics Unit under Bernard Meltzer (circa 1916–2008). The Metamathematics Unit is famous for the work of Robert Boyer and J Strother Moore in mechanized theorem proving and of Robert Kowalski in developing some of the principles of logic programming.8 At IBM in Poughkeepsie, Nathan Rochester and Herb Gelernter continued AI research for a short time after the Dartmouth workshop. This research resulted in the geometry-theorem-proving machine. However, soon after, according to a book about government support for computing research, “in spite of the early activity of Rochester and other IBM researchers, the corporation’s interest in AI cooled. Although work continued on computer-based checkers and chess, an internal report prepared about 1960 took a strong position against broad support for AI.”9 Perhaps IBM wanted to emphasize how computers helped people perform tasks rather than how they might replace people. McCarthy’s view about all of this is that “IBM thought that artificial intelligence [that machines were as smart as people] was bad for IBM’s image. . . This may have been associated with one of their other image slogans, which was ‘data processing, not computing.’”10 8.3 Research Support As the computing systems needed for AI research became larger and more expensive, and as AI laboratories formed, it became necessary to secure more financial support than was needed in the days when individual investigators began work in the field. Two of the major sources of funding during the late 1950s and early 1960s were the Office of Naval Research (ONR) and the Advanced Research Projects Agency (ARPA), each a part of the U.S. defense establishment. ONR was formed shortly after the end of the Second World War. Its mission was “to plan, foster, and encourage scientific research in recognition of its paramount importance as related to the maintenance of future naval power and the preservation of national security.” Its Information Systems Branch was set up in the mid-1950s under the direction of Marshall Yovits. The branch supported AI work at several institutions and also sponsored conferences and workshops on self-organizing systems, cybernetics, optical character 160 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 161. 8.3 Research Support recognition, and artificial intelligence. All of this was done in anticipation that these technologies would be generally useful to the U.S. Navy. (A later director, Marvin Denicoff, supported some of my research and my AI textbook writing.) The formation of ARPA was, in part, a response to the successful launch of the Soviet satellite Sputnik in 1957. ARPA’s mission was to provide significant amounts of research funds to attack problem areas important to U.S. defense. One of its most important projects in the late 1950s was the development of ablative nose cones to absorb and dissipate heat during ballistic missile reentry. Its Information Processing Techniques Office (IPTO) was set up in 1962 under the direction of J. C. R. (Lick) Licklider (1915–1990; Fig. 8.4). “Lick” (as he was called by all who knew him) was a psychoacoustician who worked first at Lincoln Laboratory and MIT and later at BBN. Lick’s 1960 paper, “Man-Computer Symbiosis,” proposed that men and computers should “cooperate in making decisions and controlling complex situations without inflexible dependence on predetermined programs.”11 Lick was persuaded that computers would play a very important role in defense – especially in those applications in which people and computers worked together. At ARPA, he provided funds to MIT for the formation of Project MAC (an acronym for Machine-Aided Cognition and perhaps for Multi-Access Computing or Man And Computers). [Project MAC, initially founded in July 1963, was later to become the Laboratory for Computer Science (LCS), and still later to evolve into the Computer Science and Artificial Intelligence Laboratory (CSAIL).] Project MAC took Minsky and McCarthy’s Artificial Intelligence Project under its wing and also supported the development of MIT’s Compatible Time-Sharing System (CTSS) under Fernando Corbató. (CTSS work was separate from McCarthy’s time-sharing project.) ARPA funds helped to establish “centers of excellence” in computer science. Besides MIT, these centers included Stanford, Carnegie Mellon, and SRI. ARPA also supported computer science work at the RAND Corporation, the Systems Development Corporation, and BBN, among others. AI was just one of ARPA’s interests. IPTO also supported research that led to graphical user interfaces (and the mouse), supercomputing, computer hardware and very-large-scale integrated circuits (VLSI), and, perhaps most famously, research that led to the Internet. According to Licklider, “ARPA budgets did not even include AI as a separate line item until 1968.”12 But as far as AI was concerned, Lick believed that Newell and Simon, Minsky, and McCarthy ought to be provided with research funds adequate to support big AI projects. With regard to the situation at Stanford (and probably to that at MIT and CMU also), Paul Edwards explained that13 [F]unding from ARPA was virtually automatic; Licklider simply Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 161
- 162. 8 1960s’ Infrastructure Figure 8.4: J. C. R. Licklider. (Photograph by Koby-Antupit from MIT Collec- tion (JCL8).) asked McCarthy what he wanted and then gave it to him, a procedure unthinkable for most other government agencies. Licklider remembered that ‘it seemed obvious to me that he should have a laboratory supported by ARPA. . . . So I wrote him a contract at that time.” McCarthy remembers all of this somewhat differently. Soon after arriving at Stanford in 1962, he sent a proposal to Licklider “to do AI.” McCarthy claims that Licklider demurred at first – citing their close relationship when McCarthy was at MIT and Licklider at BBN – but then gave him “a small contract.”14 But perhaps it was not so “small” compared with how research was usually supported (say by the National Science Foundation) at the time. Les Earnest claims that McCarthy “obtained financial support for a small activity (6 persons) from the Advanced Research Projects Agency (ARPA) beginning June 15, 1963.”15 162 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 163. 8.4 All Dressed Up and Places to Go Later, ARPA was renamed DARPA (for Defense Advanced Research Projects Agency) to emphasize its role in defense-related research. DARPA projects and grants were typically much larger than those of ONR and allowed the purchase of computers and other equipment as well as support for personnel. It’s hardly an exaggeration to say that a good part of today’s computer-based infrastructure is the result of DARPA research support. 8.4 All Dressed Up and Places to Go By the mid-1960s AI was well prepared for further advances. Flushed with early successes it was poised to make rapid progress during the rest of the 1960s and 1970s. Indeed, many people made enthusiastic predictions. For example, in a 1957 talk16 Herb Simon predicted that within ten years “a digital computer will be the world’s chess champion unless the rules bar it from competition.” He made three other predictions too. Within ten years computers would compose music, prove a mathematical theorem, and embody a psychological theory as a program. He said “it is not my aim to surprise or shock you. . . but the simplest way I can summarize is to say that there are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until – in a visible future – the range of problems they can handle will be coextensive with the range to which the human mind has been applied.”17 Later Simon said that his predictions were part of an attempt “to give some feeling for what computers would mean” to society. One could argue that Simon’s predictions about computers composing music and proving a mathematical theorem were realized soon after he made them, but a computer chess champion was not to emerge until forty years later. And, we are still far, I think, from achieving things “coextensive” with what the human mind can achieve. Simon was not alone in being optimistic. According to Hubert Dreyfus, “Marvin Minsky, head of MITs Artificial Intelligence Laboratory, declared in a 1968 press release for Stanley Kubrick’s movie, 2001: A Space Odyssey, that ‘in 30 years we should have machines whose intelligence is comparable to man’s.’ ”18 The difficulty in assessing these sorts of predictions is that “human-level intelligence” is multifaceted. By the year 2000, AI programs did outperform humans in many intellectual feats while still having a long way to go in most others. Even so, what had already been accomplished was an impressive start. More important perhaps than the specific demonstrations of intelligent behavior by machines was the technical base developed during the 1950s and early 1960s. AI researchers now had the means to represent knowledge by encoding it in networks, as logical formulas, or in other symbol structures tailored to specific problem areas. Furthermore, they had accumulated Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 163
- 164. 8 NOTES experience with heuristic search and other techniques for manipulating and using that knowledge. Also, researchers now had new programming languages, IPL, LISP, and POP-2, that made it easier to write symbol-processing programs. Complementing all of this symbol-processing technology were neural networks and related statistical approaches to pattern recognition. These technical assets, along with the organizational and financial ones, provided a solid base for the next stage of AI’s development. Notes 1. For McCarthy’s own history of the development of LISP, see http://www-formal.stanford.edu/jmc/history/lisp.html. Also see Herbert Stoyan’s history of LISP at http://www8.informatik.uni-erlangen.de/html/lisp-enter.html. [156] 2. See McCarthy’s memo proposing how to build a time-sharing system at http://www-formal.stanford.edu/jmc/history/timesharing-memo.html. [157] 3. For more about these early days of computing at MIT and of time-sharing work there (among other things), see the interview with John McCarthy conducted by William Aspray of the Charles Babbage Institute on March 2, 1989. It is available online at http://www.cbi.umn.edu/oh/display.phtml?id=92. [157] 4. From an interview conducted by Arthur L. Norberg on November 1, 1989, for the Charles Babbage Institute. Available online at http://www.cbi.umn.edu/oh/display.phtml?id=107. [157] 5. For a history of AI work in the lab up to 1973, see Lester Earnest (ed.), “Final Report: The First Ten Years of Artificial Intelligence Research at Stanford,” Stanford Artificial Intelligence Laboratory Memo AIM-228 and Stanford Computer Science Department Report No. STAN-CS-74-409, July 1973. (Available online at http://www-db.stanford.edu/pub/cstr/reports/cs/tr/74/409/CS-TR-74-409.pdf.) For other SAIL history, see “SAIL Away” by Les Earnest at http://www.stanford.edu/∼learnest/sailaway.htm. [158] 6. A textscript of the interview can be found online at http://www.aiai.ed.ac.uk/events/ccs2002/CCS-early-british-ai-dmichie.pdf. [158] 7. Donald Michie, “Experiments on the Mechanisation of Game Learning: 1. Characterization of the Model and its Parameters,” Computer Journal, Vol. 1, pp. 232–263, 1963. [158] 8. For a history of these Edinburgh groups, see Jim Howe’s online 1994 article “Artificial Intelligence at Edinburgh University: A Perspective” at http://www.dai.ed.ac.uk/AI at Edinburgh perspective.html. [160] 9. National Research Council, Funding a Revolution: Government Support for Computing Research, Washington, DC: National Academy Press, 1999. (An html version of this book, which contains a rather conservative account of AI history, is available from http://www.nap.edu/catalog.php?record id=6323#toc.) [160] 10. From “An Interview with John McCarthy,” conducted by William Aspray on 2 March 1989, Palo Alto, CA, Charles Babbage Institute, The Center for the History of Information Processing, University of Minnesota, Minneapolis. [160] 11. J. C. R. Licklider, “Man–Computer Symbiosis,” IRE Transactions on Human Factors in Electronics, HFE-1, pp. 4–11, 1960. Available online at http://memex.org/licklider.html. [161] 164 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 165. 8.4 NOTES 12. J. C. R. Licklider, “The Early Years: Founding IPTO,” p. 220 in Thomas C. Bartee (ed.), Expert Systems and Artificial Intelligence: Applications And Management, Indianapolis: Howard W. Sams, 1988. [161] 13. Paul Edwards, The Closed World: Computers and the Politics of Discourse in Cold War America, p. 270, Cambridge, MA: MIT Press, 1996. [161] 14. From “An Interview with John McCarthy,” op. cit. [162] 15. Lester Earnest (ed.), “Final Report: The First Ten Years of Artificial Intelligence Research at Stanford,” Stanford Artificial Intelligence Laboratory Memo AIM-228 and Stanford Computer Science Department Report No. STAN-CS-74-409, July 1973. (Available online at http://www-db.stanford.edu/pub/cstr/reports/cs/tr/74/409/CS-TR-74-409.pdf.) [162] 16. 12th National Meeting of the Operations Research Society (ORSA) in Pittsburgh. [163] 17. The published version of this talk is in Herbert Simon and Allen Newell, “Heuristic Problem Solving: The Next Advance in Operations Research,” Operations Research, Vol. 6, January–February 1958. [163] 18. Hubert L. Dreyfus, “Overcoming the Myth of the Mental,” Topoi, Vol. 25, pp. 43–49, 2006. [163] Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 165
- 166. 8 NOTES 166 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 167. 8.4 Part III Efflorescence: Mid-1960s to Mid-1970s During the 1960s and well into the 1970s, AI research blossomed and progress seemed rapid. The laboratories established at MIT, Carnegie Mellon, Stanford, SRI, and Edinburgh expanded, and several new groups got started at other universities and companies. Achievements during the preceding years, even though modest in retrospect, were exciting and full of promise, which enticed several new people into the field, myself included. Many of us were just as optimistic about success as Herb Simon and Marvin Minsky were when they made their predictions about rapid progress. AI entered a period of flowering that led to many new and important inventions. Several ideas originated in the context of Ph.D. dissertation research projects. Others emerged from research laboratories and from individual investigators wrestling with theoretical problems. In this part, I’ll highlight some of the important projects and research results. Although not a complete account, they typify much of what was going on in AI during the period. I’ll begin with work in computer vision. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 167
- 168. 8 168 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 169. 9.0 Chapter 9 Computer Vision Sighted humans get much of their information through vision. That part of AI called “computer vision” (or, sometimes, “machine vision”) deals with giving computers this ability. Most computer vision work is based on processing two-dimensional images gathered from a three-dimensional world – images gathered by one or more television cameras, for example. Because the images are two-dimensional projections of a three-dimensional scene, the imaging process loses information. That is, different three-dimensional scenes might produce the same two-dimensional image. Thus, the problem of reconstructing the scene faithfully from an image is impossible in principle. Yet, people and other animals manage very well in a three-dimensional world. They seem to be able to interpret the two-dimensional images formed on their retinas in a way that gives them reasonably accurate and useful information about their environments. Stereo vision, using two eyes, helps provide depth information. Computer vision too can use “stereopsis” by employing two or more differently located cameras looking at the same scene. (The same effect can be achieved by having one camera move to different positions.) When two cameras are used, for example, the images formed by them are slightly displaced with respect to each other, and this displacement can be used to calculate distances to various parts of the scene. The computation involves comparing the relative locations in the images that correspond to the objects in the scene for which depth measurements are desired. This “correspondence problem” has been solved in various ways, one of which is to seek high correlations between small areas in one image with small areas in the other. Once the “disparity” of the location of an image feature in the two images is known, the distance to that part of the scene giving rise to this image feature can be calculated by using trigonometric calculations (which I won’t go into here.)1 Perhaps surprisingly, a lot of depth information can be obtained from Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 169
- 170. 9 Computer Vision other cues besides stereo vision. Some of these cues are inherent in a single image, and I’ll be describing these in later chapters. Even more importantly, background knowledge about the kinds of objects one is likely to see accounts for much of our ability to interpret images. Consider the image shown in Fig. 9.1 for example. Figure 9.1: Two tables. (Illustration courtesy of Michael Bach.) Most people would describe this image as being of two tables, one long and narrow and the other more-or-less square. Yet, if you measure the actual table tops in the image itself, you might be surprised to find that they are exactly the same size and shape! (The illustration is based on an illusion called “turning the tables” by the psychologist Roger Shepherd and is adapted from Michael Bach’s version of Shepherd’s diagram. If you visit Bach’s Web site, http://www.michaelbach.de/ot/sze shepardTables/, you can watch while one table top moves over to the other without changing shape.) Something apart from the image provides us with information that induces us to make inferences about the shapes of the three-dimensional tables captured in the two-dimensional image shown in Fig. 9.1. As we shall see, that extra information consists of two things: knowledge about the image-forming process under various lighting conditions and knowledge about the kinds of things and their surfaces that occur in our three-dimensional world. If we could endow computers with this sort of knowledge, perhaps they too would be able to see. 170 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 171. 9.1 Hints from Biology 9.1 Hints from Biology There has been a steady flow of information back and forth between scientists attempting to understand how vision works in animals and engineers working on computer vision. An early example of work at the intersection of these two interests was described in an article titled “What the Frog’s Eye Tells the Frog’s Brain”2 by four scientists at MIT. Guided by previous biological work, the four, Jerome Lettvin, H. R. Maturana, Warren McCulloch, and Walter Pitts, probed the parts of the frog’s brain that processed images. They found that the frog’s visual system consisted of “detectors” that responded only to certain kinds of things in its visual field. It had detectors for small, moving convex objects (such as flies) and for a sudden darkening of illumination (such as might be caused by a looming predator). These, together with a couple of other simple detectors, gave the frog information about food and danger. In particular, the frog’s visual system did not, apparently, construct a complete three-dimensional model of its visual scene. As the authors wrote, The frog does not seem to see or, at any rate, is not concerned with the detail of stationary parts of the world around him. He will starve to death surrounded by food if it is not moving. His choice of food is determined only by size and movement. He will leap to capture any object the size of an insect or worm, providing it moves like one. He can be fooled easily not only by a bit of dangled meat but by any moving small object. His sex life is conducted by sound and touch. His choice of paths in escaping enemies does not seem to be governed by anything more devious than leaping to where it is darker. Since he is equally at home in water and on land, why should it matter where he lights after jumping or what particular direction he takes? Other experiments produced further information about how the brain processes visual images. Neurophysiologists David Hubel (1926– ) and Torsten Wiesel (1924– ) performed a series of experiments, beginning around 1958, which showed that certain neurons in the mammalian visual cortex responded selectively to images and parts of images of specific shapes. In 1959 they implanted microelectrodes in the primary visual cortex of an anesthetized cat. They found that certain neurons fired rapidly when the cat was shown images of small lines at one angle and that other neurons fired rapidly in response to small lines at another angle. In fact, they could make a “map” of this area of the cat’s brain, relating neuron location to line angle. They called these neurons “simple cells” – to be distinguished from other cells, called “complex cells,” that responded selectively to lines moving in a certain direction. Later work revealed that other neurons were specialized to respond to images containing more complex shapes such as corners, longer lines, and large edges.3 They found that similar specialized neurons also existed in the brains Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 171
- 172. 9 Computer Vision of monkeys.4 Hubel and Wiesel were awarded the Nobel Prize in Physiology or Medicine in 1981 (jointly with Roger Sperry for other work).5 As I’ll describe in later sections, computer vision researchers were developing methods for extracting lines (both large and small) from images. Hubel and Wiesel’s work helped to confirm their view that finding lines in images was an important part of the visual process. Yet, straight lines seldom occur in the natural environments in which cats (and humans) evolved, so why do they (and we) have neurons specialized for detecting them? In fact, in 1992 the neuroscientists Horace B. Barlow and David J. Tolhurst wrote a paper titled “Why Do You Have Edge Detectors?”6 As a possible answer to this question, Anthony J. Bell and Terrence J. Sejnowski later showed mathematically that natural scenes can be analyzed as a weighted summation of small edges even though the scenes themselves do not have obvious edges.7 9.2 Recognizing Faces In the early 1960s at his Palo Alto company, Panoramic Research, Woodrow (Woody) W. Bledsoe (who later did work on automatic theorem proving at the University of Texas), along with Charles Bisson and Helen Chan (later Helen Chan Wolf), developed techniques for face recognition supported by projects from the CIA.8 Here is a description of their approach taken from a memorial article:9 This [face-recognition] project was labeled man-machine because the human extracted the coordinates of a set of features from the photographs, which were then used by the computer for recognition. Using a GRAFACON, or RAND TABLET, the operator would extract the coordinates of features such as the center of pupils, the inside corner of eyes, the outside corner of eyes, point of widows peak, and so on. From these coordinates, a list of 20 distances, such as width of mouth and width of eyes, pupil to pupil, were computed. These operators could process about 40 pictures an hour. When building the database, the name of the person in the photograph was associated with the list of computed distances and stored in the computer. In the recognition phase, the set of distances was compared with the corresponding distance for each photograph, yielding a distance between the photograph and the database record. The closest records are returned. Bledsoe continued this work with Peter Hart at SRI after leaving Panoramic in 1966.10 Then, in 1970, a Stanford Ph.D. student, Michael D. Kelly, wrote a computer program that was able automatically to detect facial features in 172 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 173. 9.3 Computer Vision of Three-Dimensional Solid Objects pictures and use them to identify people.11 The task for his program was, as he put it, to choose, from a collection of pictures of people taken by a TV camera, those pictures that depict the same person. . . . In brief, the program works by finding the location of features such as eyes, nose, or shoulders in the pictures. . . . The interesting and difficult part of the work reported in this thesis is the detection of these features in digital pictures. The nearest-neighbor method is used for identification of individuals once a set of measurements has been obtained. Another person who did pioneering work in face recognition was vision researcher Takeo Kanade, now a professor at Carnegie Mellon University. In a 2007 speech at the Eleventh IEEE International Conference on Computer Vision, he reflected on his early work in this field:12 “I wrote my face recognition program in an assembler language, and ran it on a machine with 10 microsecond cycle time and 20 kB of main memory. It was with pride that I tested the program with 1000 face images, a rare case at the time when testing with 10 images was called a ‘large-scale’ experiment.” (By the way, Kanade has continued his face recognition work up to the present time. His face-recognition Web page is at http://www.ri.cmu.edu/labs/lab 51.html.) Face recognition programs of the 1960s and 1970s had several limitations. They usually required that images be of faces of standard scale, pose, expression, and illumination. Toward the end of the book, I’ll describe research leading to much more robust automatic face recognition. 9.3 Computer Vision of Three-Dimensional Solid Objects 9.3.1 An Early Vision System Lawrence G. Roberts (1937– ), an MIT Ph.D. student working at Lincoln Laboratory, was perhaps the first person to write a program that could identify objects in black-and-white (gray-scale) photographs and determine their orientation and position in space. (His program was also the first to use a “hidden-line” algorithm, so important in subsequent work in computer graphics. As chief scientist and later director of ARPA’s Information Processing Techniques Office, Roberts later played an important role in the creation of the Arpanet, the forerunner of the Internet.) In the introduction to his 1963 MIT Ph.D. dissertation,13 Roberts wrote Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 173
- 174. 9 Computer Vision The problem of machine recognition of pictorial data has long been a challenging goal, but has seldom been attempted with anything more complex than alphabetic characters. Many people have felt that research on character recognition would be a first step, leading the way to a more general pattern recognition system. However, the multitudinous attempts at character recognition, including my own, have not led very far. The reason, I feel, is that the study of abstract, two-dimensional forms leads us away from, not toward, the techniques necessary for the recognition of three-dimensional objects. The perception of solid objects is a process which can be based on the properties of three-dimensional transformations and the laws of nature. By carefully utilizing these properties, a procedure has been developed which not only identifies objects, but also determines their orientation and position in space. Roberts’s system first processed a photograph of a scene to produce a representation of a line drawing. It then transformed the line drawing into a three-dimensional representation. Matching this representation against a stored list of representations of solid objects allowed it to classify the object it was viewing. It could also produce a computer-graphics image of the object as it might be seen from any point of view. Our main interest here is in how Roberts processed the photographic image. After scanning the photograph and representing it as an array of numbers (pixels) representing intensity values, Roberts used a special calculation, later called the “Roberts Cross,” to determine whether or not each small 2 × 2 square in the array corresponded to a part of the image having an abrupt change in image intensity. (The Roberts Cross was the first example of what were later called “gradient operators.”) He then rerepresented the image “lighting up” only those parts of the image where the intensity changed abruptly and leaving “dark” those parts of the image with more-or-less uniform intensity. The result of this process is illustrated in Fig. 9.2 for a typical image used in Roberts’s dissertation. As can be seen in that figure, large changes in image intensity are usually associated with the edges of objects. Thus, gradient operators, such as the Roberts Cross, are often called “edge detectors.” Further processing of the image on the right attempted to connect the dots representing abrupt intensity changes by small straight-line segments, then by longer line segments. Finally, a line drawing of the image was produced. This final step is shown in Fig. 9.3. Roberts’s program was able to analyze many different photographs of solid objects. He commented that “The entire picture-to-line-drawing process is not optimal but works for simple pictures.” Roberts’s success stimulated further work on programs for finding lines in images and for assembling these lines into representations of objects. Perhaps primed by Roberts’s choice of 174 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 175. 9.3 Computer Vision of Three-Dimensional Solid Objects Figure 9.2: Detecting changes in intensity. (Photographs used with permission of Lawrence Roberts.) Figure 9.3: Producing the final line drawing. (Photographs used with permission of Lawrence Roberts.) solid objects, much of the subsequent work dealt with toy blocks (or “bricks” as they are called in Britain). 9.3.2 The “Summer Vision Project” Interestingly, Larry Roberts was a student of MIT information theory professor Peter Elias, not of Marvin Minsky. But Minsky’s group soon began to work on computer vision also. In the summer of 1966, the mathematician and psychologist Seymour Papert, a recent arrival at MIT’s Artificial Intelligence Group, launched a “summer vision project.” Its goal was to develop a suite of programs that would analyze a picture from a “videsector” (a kind of scanner) to “actually name objects [such as balls, cylinders, and blocks] by matching them with a vocabulary of known objects.” One motivation for the project was “to use our summer workers effectively in the Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 175
- 176. 9 Computer Vision construction of a significant part of a visual system.”14 Of course, the problem of constructing “a significant part of a visual system” was much more difficult than Papert expected. Nevertheless, the project was successful in that it began a sustained effort in computer vision research at MIT, which continues to this day. After these early forays at MIT (and similar ones at Stanford and SRI to be described shortly), computer vision research focused on two areas. The first was what might be called “low-level” vision – those first stages of image processing that were aimed at constructing a representation of the image as a line drawing, given an image that was of a scene containing rather simple objects. The second area was concerned with how to analyze the line drawing as an assemblage of separate objects that could be located and identified. An important part of low-level vision was “image filtering,” to be described next. 9.3.3 Image Filtering The idea of filtering an image to simplify it, to correct for noise, and to enhance certain image features had been around for a decade or more. I have already mentioned, for example, that in 1955 Gerald P. Dinneen processed images to remove noise and enhance edges. Russell Kirsch and colleagues had also experimented with image processing.15 (Readers who have manipulated their digital photography pictures on a computer have used some of these image filters.) Filtering two-dimensional images is not so very different from filtering one-dimensional electronic signals – a commonplace operation. Perhaps the simplest operation to describe is “averaging,” which blurs fine detail and removes random noise specks. As in all averaging operations, image averaging takes into account adjacent values and combines them. Consider, for example, the image array of intensity values shown in Fig. 9.4 containing a 3 × 3 “averaging window” outlined in bold. These intensity values correspond to an image whose right side is bright and whose left side is dark with a sharp edge between. (I adopt the convention that large numbers, such as 10 correspond to brightly illuminated parts of the image, and the number 0 corresponds to black.) The averaging operation moves the averaging window over the entire image so that its center lies over each pixel in turn. For each placement of the window, the value of the intensity at its center is replaced (in the filtered version) by the average intensity of the values within the window. (The process of moving a window around the image and doing calculations based on the numbers in the window is called convolution.) In this example, the 0 at the center of the window would be replaced by 3.33 (perhaps rounded down to 3). One can see that averaging blurs the sharp edge – with the 10 fading to (a rounded) 7 fading to 3 fading to 0 as one moves from right to left. However, intensities well within evenly illuminated regions are not changed. 176 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 177. 9.3 Computer Vision of Three-Dimensional Solid Objects Figure 9.4: An array of image intensity values and an averaging window. I have already mentioned another important filtering operation, the Roberts Cross, for detecting abrupt brightness changes in an image. Another one was developed in 1968 by a Ph.D. student at Stanford, Irwin Sobel. It was dubbed the “Sobel Operator” by Raj Reddy who described it in a Computer Vision course at Stanford.16 The operator uses two filtering windows – one sensitive to large gradients (intensity changes) in the vertical direction and one to large gradients in the horizontal direction. These are shown in Fig. 9.5. Figure 9.5: Sobel’s vertical (left) and horizontal (right) filters. Each of the Sobel filters works the same way as the averaging filter, except that the image intensity at each point is multiplied by the number in the corresponding cell of the filtering window before adding all of the numbers. The sum would be 0 inside regions of uniform illumination. If the vertical filter is centered over a vertical edge (with the right side brighter than the left), the sum would be positive. (I’ll let you think about the other possibilities.) Results from the two filtering windows are combined mathematically to detect abrupt changes in any direction. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 177
- 178. 9 Computer Vision The images in Fig. 9.6 illustrate the Sobel Operator. The image on the right is the result of applying the Sobel Operator to the image on the left. Figure 9.6: Finding abrupt changes in image brightness with the Sobel Operator. (Photographs taken by George Miller and available at http://en.wikipedia.org/ wiki/Sobel operator. Used under the terms of the GNU Free Documentation License.) A number of other more complex and robust image processing operations have been proposed and used for finding edges, lines, and vertices of objects in images.17 A particularly interesting one for finding edges was proposed by the British neuroscientist and psychologist David Marr (1945–1980) and Ellen Hildreth.18 The Marr–Hildreth edge detector uses a filtering window called a “Laplacian of Gaussian (LoG).” (The name arises because a mathematical operator called a “Laplacian” is used on a bell-shaped curve called a “Gaussian,” commemorating two famous mathematicians, namely, Pierre-Simon Laplace and Carl Friedrich Gauss.) In Fig. 9.7, I show an example of LoG numbers in a 9 × 9 filtering window. This window is moved around an image, multiplying image numbers and adding them up, in the same way as the other filtering windows I have already mentioned. Figure 9.7: A Laplacian of Gaussian filtering window. 178 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 179. 9.3 Computer Vision of Three-Dimensional Solid Objects If LoG numbers are plotted as “heights” above (and below) a plane, an interesting-looking surface results. An example is shown in Fig. 9.8. This LoG function is often called, not surprisingly, a Mexican hat or sombrero function. Figure 9.8: A Laplacian of Gaussian surface. Figure 9.9: An image (left) and its LoG-processed version (right). (Images taken from David Marr and E. Hildreth, “Theory of Edge Detection,” Proceedings of the Royal Society of London, Series B, Biological Sciences, Vol. 207, No. 1167, p. 198, February 1980.) Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 179
- 180. 9 Computer Vision Marr and Hildreth used the LoG filtering window on several example images. One example, taken from their paper, is shown in Fig 9.9. Notice that the image on the right has whitish bands surrounding darker parts of the image. The Marr–Hildreth edge detector employs a second image-processing operation that looks for the transitions from light to dark (and vice versa) in the LoG-processed image to produce a final “line drawing,” as shown in Fig. 9.10. Figure 9.10: The final result of a Marr–Hildreth edge-detecting operation. (From David Marr and E. Hildreth, “Theory of Edge Detection,” Proceedings of the Royal Society of London, Series B, Biological Sciences, Vol. 207, No. 1167, p. 198, February 1980.) Further advances have been made in edge detection since Marr and Hildreth’s work. Among the currently best detectors are those related to one proposed by John Canny called the Canny edge detector.19 As a neurophysiologist, Marr was particularly interested in how the human brain processes images. In a 1976 paper,20 he proposed that the first stage of processing produces what he called a “primal sketch.” As he puts it in his summary of that paper, It is argued that the first step of consequence is to compute a primitive but rich description of the grey-level changes present in an image. The description is expressed in a vocabulary of kinds of intensity change (EDGE, SHADING-EDGE, EXTENDED-EDGE, LINE, BLOB etc.). . . . This description is obtained from the intensity array by fixed techniques, and it is called the primal sketch. Marr and Hildreth put forward their edge detector as one of the operations the brain uses in producing a primal sketch. They stated that their 180 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 181. 9.3 Computer Vision of Three-Dimensional Solid Objects theory “explains several basic psychophysical findings, and. . . forms the basis for a physiological model of simple [nerve] cells.” Marr’s promising career in vision research ended when he succumbed to cancer in 1980. During the last years of his life he completed an important book detailing his theories of human vision.21 I’ll describe some of Marr’s ideas about other visual processing steps in a subsequent chapter. 9.3.4 Processing Line Drawings Assuming, maybe somewhat prematurely, that low-level vision routines could produce a line-drawing version of an image, many investigators moved on to develop methods for analyzing line drawings to find objects in images. Adolfo Guzman-Arenas (1943– ), a student in Minsky’s AI Group, focused on how to segment a line drawing of a scene containing blocks into its constituents, which Guzman called “bodies.” His LISP program for accomplishing this separation was called SEE and ran on the MIT AI Group’s PDP-6 computer.22 The input to SEE was a line-drawing representation of a scene in terms of its surfaces, lines (where two surfaces came together), and vertices (where lines came together). SEE’s analysis of a scene began by sorting its vertices into a number of different types. For each vertex, depending on its type, SEE connected adjacent planar surfaces with “links.” The links between surfaces provide evidence that those surfaces belong to the same body. For example, some links for a scene analyzed by SEE are shown in Fig. 9.11. SEE performed rather well on a wide variety of line drawings. For example, it correctly found all of the bodies in the scene shown in Fig. 9.12. For most of his work, Guzman assumed that somehow other programs would produce his needed line drawings from actual images. As he wrote in a paper describing his research,23 The scene itself is not obtained from a visual input device, or from an array of intensities of brightness. Rather, it is assumed that a preprocessing of some sort has taken place, and the scene to be analyzed is available in a symbolic format. . . in terms of points (vertices), lines (edges), and surfaces (regions).” Additionally, Guzman did not concern himself with what might be done after the scene had been separated into bodies: . . . it cannot find “cubes” or “houses” in a scene, since it does not know what a “house” is. Once SEE has partitioned a scene into bodies, some other program will work on them and decide which of those bodies are “houses.” Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 181
- 182. 9 Computer Vision Figure 9.11: Links established by SEE for a sample scene. (Illustration used with permission of Adolpho Guzman.) Later extensions to SEE, reported in the final version of his thesis, involved some procedures for image capture. But the images were of specially prepared scenes, as he recently elaborated:24 Originally SEE worked on hand-drawn scenes, “perfect scenes” (drawings of lines). . . Later, I constructed a bunch of wooden polyhedra (mostly irregular), painted them black, carefully painted their edges white, piled several of them together, and took pictures of the scenes. The pictures were scanned, edges found, and given to SEE. It worked quite well on them. Although SEE was capable of finding bodies in rather complex scenes, it also could make mistakes, and it could not identify blocks that had holes in them. The next person to work on the problem of scene articulation was David Huffman (1925–1999), a professor of Electrical Engineering at MIT. (Huffman was famous for his invention, while a graduate student at MIT, of what came to be called “Huffman coding,” an efficient scheme that is used today in many applications involving the compression and transmission of digital data.) Huffman was bothered by what he considered Guzman’s incomplete analysis of what kinds of objects could correspond to what kinds of line drawings. After 182 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 183. 9.3 Computer Vision of Three-Dimensional Solid Objects Figure 9.12: A scene analyzed by SEE. (Illustration used with permission of Adolpho Guzman.) leaving MIT in 1967 to become a professor of Information and Computer Science at the University of California at Santa Cruz, he completed a theory for assigning labels to the lines in drawings of trihedral solids – objects in which exactly three planar surfaces join at each vertex of the object. The labels depended on the ways in which planes could come together at a vertex. (I got to know Huffman well at that time because he consulted frequently at the Stanford Research Institute.) Huffman pointed out that there are only four ways in which three plane surfaces can come together at a vertex.25 These are shown in Fig. 9.13. In addition to these four kinds of vertices, a scene might contain what Huffman called “T-nodes” – line intersection types caused by one object in a scene occluding another. These all give rise to a number of different kinds of labels for the lines in the scene; these labels specify whether the lines correspond to convex, concave, or occluding edges. Huffman noted that the labels of the lines in a drawing might be locally consistent (around some vertices) but still be globally inconsistent (around all of the vertices). Consider, for example, Roger Penrose’s famous line drawing of an “impossible object” shown in Fig. 9.14.26 (It is impossible because no three-dimensional object, viewed in “general position,” could produce this image.) No “real scene” can have a line with two different labels. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 183
- 184. 9 Computer Vision Figure 9.13: The four different kinds of vertices that can occur in trihedral solids. Figure 9.14: An impossible object. 184 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 185. 9.3 Computer Vision of Three-Dimensional Solid Objects Max Clowes (circa 1944–1981) of Sussex University in Britain developed similar ideas independently,27 and the labeling scheme is now generally known as Huffman–Clowes labeling. Next comes David Waltz (1943– ). In his 1972 MIT Ph.D. thesis, he extended the Huffman–Clowes line-labeling scheme to allow for line drawings of scenes with shadows and possible “cracks” between two adjoining objects.28 Waltz’s important contribution was to propose and implement an efficient computational method for satisfying the constraint that all of the lines must be assigned one and only one label. (For example, an edge can’t be concave at one end and convex at the other.) In Fig. 9.15, I show an example of a line drawing that Waltz’s program could correctly segment into its constituents. Figure 9.15: A scene with shadows analyzed by Waltz’s program. (Illustration used with permission of David Waltz.) Summarizing some of the work on processing line drawings at MIT, Patrick Winston says that “Guzman was the experimentalist, Huffman the theoretician, and Waltz the encyclopedist (because Waltz had to catalog thousands of junctions, in order to deal with cracks and shadows).”29 Meanwhile, similar work for finding, identifying, and describing objects in three-dimensional scenes was being done at Stanford. By 1972 Electrical Engineering Ph.D. student Gilbert Falk could segment scenes of line drawings into separate objects using techniques that were extensions of those of Guzman.30 And by 1973, Computer Science Ph.D. student Gunnar Grape performed segmentation of scenes containing parallelepipeds and wedges using models of those objects.31 Other work on analysis of scenes containing polyhedra was done by Yoshiaki Shirai while he was visiting MIT’s AI Lab32 and by Alan Mackworth at the Laboratory of Experimental Psychology of the University of Sussex.33 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 185
- 186. 9 NOTES Notes 1. For a thorough treatment, see David Forsyth and Jean Ponce, Computer Vision: A Modern Approach, Chapter 13, Upper Saddle River, NJ: Prentice Hall, 2003. [169] 2. Lettvin et al., “What the Frog’s Eye Tells the Frog’s Brain,” Proceedings of the IRE, Vol. 47, No. 11, pp. 1940–1951, 1959. [Reprinted as Chapter 7 in William C. Corning and Martin Balaban (eds.), The Mind: Biological Approaches to Its Functions, pp. 233–258, 1968.] [171] 3. David H. Hubel and Torsten N. Wiesel, “Receptive Fields, Binocular Interaction and Functional Architecture in the Cat’s Visual Cortex,” Journal of Physiology, Vol. 160, pp. 106–154, 1962. [171] 4. David H. Hubel and Torsten N. Wiesel, “Receptive Fields and Functional Architecture of Monkey Striate Cortex, Journal of Physiology, Vol. 195, pp. 215–243, 1968. [172] 5. An interesting account of Hubel’s and Wiesel’s work and descriptions about how the brain processes visual images can be found in Hubel’s online book Eye, Brain, and Vision at http://neuro.med.harvard.edu/site/dh/index.html. [172] 6. Horace B. Barlow and D. J. Tolhurst, “Why Do You Have Edge Detectors?,” in Proceedings of the 1992 Optical Society of America Annual Meeting, Technical Digest Series, Vol. 23, pp. 172, Albuquerque, NM, Washington: Optical Society of America, 1992. [172] 7. Anthony J. Bell and Terrence J. Sejnowski, “Edges Are the ‘Independent Components’ of Natural Scenes,” Advances in Neural Information Processing Systems, Vol. 9, Cambridge, MA: MIT Press, 1996. Available online at ftp://ftp.cnl.salk.edu/pub/tony/edge.ps.Z. [172] 8. Woodrow W. Bledsoe and Helen Chan, “A Man–Machine Facial Recognition System: Some Preliminary Results,” Technical Report PRI 19A, Panoramic Research, Inc., Palo Alto, CA, 1965. [172] 9. Michael Ballantyne, Robert S. Boyer, and Larry Hines, “Woody Bledsoe: His Life and Legacy,” AI Magazine, Vol. 17, No. 1, pp. 7–20, 1996. Also available online at http://www.utexas.edu/faculty/council/1998-1999/memorials/Bledsoe/bledsoe.html. [172] 10. Woodrow W. Bledsoe, “Semiautomatic Facial Recognition,” Technical Report SRI Project 6693, Stanford Research Institute, Menlo Park, CA, 1968. [172] 11. Michael D. Kelly, Visual Identification of People by Computer, Stanford AI Project, Stanford, CA, Technical Report AI-130, 1970. [173] 12. http://iccv2007.rutgers.edu/TakeoKanadeResponse.htm. [173] 13. Lawrence G. Roberts, “Machine Perception of Three-Dimensional Solids,” MIT Ph.D. thesis, 1963; published as Lincoln Laboratory Technical Report #315, May 22, 1963; appears in J. T. Tippett et al. (eds.), Optical and Electro-Optical Information Processing, pp. 159–197, Cambridge, MA: MIT Press, 1965. Available online at http://www.packet.cc/files/mach-per-3D-solids.html. [173] 14. The project is described in MIT’s Artificial Intelligence Group Vision Memo No. 100 available at ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-100.pdf. [176] 15. Russell A. Kirsch et al., “Experiments in Processing Pictorial Information with a Digital Computer,” Proceedings of the Eastern Joint Computer Conference, pp. 221–229, Institute of Radio Engineers and Association Association for Computing Machinery, December 1957. [176] 16. According to Sobel, he and a fellow student, Gary Feldman, first presented the operator in a Stanford AI seminar in 1968. It was later described in Karl K. Pingle, “Visual Perception by a Computer,” in A. Grasselli (ed.), Automatic Interpretation and Classification of Images, pp. 277–284, New York: Academic Press, 1969. It was also 186 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 187. 9.3 NOTES mentioned in Richard O. Duda and Peter E. Hart, Pattern Classification and Scene Analysis, pp. 271–272, New York: John Wiley & Sons, 1973. [177] 17. See, for example, M. H. Hueckel, “An Operator Which Locates Edges in Digitized Pictures,” Journal of the ACM, Vol. 18, No. 1, pp. 113–125, January 1971, and Berthold K. P. Horn, “The Binford–Horn Line Finder,” MIT AI Memo 285, MIT, July 1971 (revised December 1973 and available online at http://people.csail.mit.edu/bkph/AIM/AIM-285-OPT.pdf). [178] 18. David Marr and Ellen Hildreth, “Theory of Edge Detection,” Proceedings of the Royal Society of London, Series B, Biological Sciences, Vol. 207, No. 1167, pp. 187–217, February 1980. [178] 19. John E. Canny, “A Computational Approach to Edge Detection,” IEEE Transactions Pattern Analysis and Machine Intelligence, Vol. 8, pp. 679–714, 1986. [180] 20. David Marr, “Early Processing of Visual Information,” Philosophical Transactions of the Royal Society of London, Series B, Biological Sciences, Vol. 275, No. 942, pp. 483–519, October 1976. [180] 21. David Marr, Vision: A Computational Investigation into the Human Representation and Processing of Visual Information, San Francisco: W.H. Freeman and Co., 1982. [181] 22. Guzman’s 1968 Ph.D. thesis is titled “Computer Recognition of Three Dimensional Objects in a Visual Scene” and is available online at http://www.lcs.mit.edu/publications/pubs/pdf/MIT-LCS-TR-059.pdf. [181] 23. Adolfo Guzman, “Decomposition of a Visual Scene into Three-Dimensional Bodies,” AFIPS, Vol. 33, pp. 291–304, Washington, DC: Thompson Book Co., 1968. Available online as an MIT AI Group memo at ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-171.pdf. [181] 24. Personal communication, September 14, 2006. [182] 25. David A. Huffman, “Impossible Objects as Nonsense Sentences,” in B. Meltzer and D. Michie (eds.), Machine Intelligence 6, pp. 195–234, Edinburgh: Edinburgh University Press, 1971, and David A. Huffman, “Realizable Configurations of Lines in Pictures of Polyhedra,” in E. W. Elcock and D. Michie (eds.), Machine Intelligence 8, pp. 493–509, Chicester: Ellis Horwood, 1977. [183] 26. According to Wikipedia, this impossible object was first drawn by the Swedish artist Oscar Reutersvärd in 1934. [183] 27. Max B. Clowes, “On Seeing Things,” Artificial Intelligence, Vol. 2, pp. 79–116, 1971. [185] 28. David L. Waltz, “Generating Semantic Descriptions from Drawings of Scenes with Shadows,” MIT AI Lab Technical Report No. AITR-271, November 1, 1972. Available online at https://dspace.mit.edu/handle/1721.1/6911. A condensed version appears in Patrick Winston (ed.), The Psychology of Computer Vision, pp. 19–91, New York: McGraw-Hill, 1975. [185] 29. Personal communication, September 20, 2006. [185] 30. Gilbert Falk, “Computer Interpretation of Imperfect Line Data as a Three-Dimensional Scene,” Ph.D. thesis in Electrical Engineering, Stanford University, Artificial Intelligence Memo AIM-132, and Computer Science Report No. CS180, August 1970. Also see Gilbert Falk, “Interpretation of Imperfect Line Data as a Three-Dimensional Scene,” Artificial Intelligence, Vol. 3, pp. 101–144, 1972. [185] 31. Gunnar Rutger Grape, “Model Based (Intermediate Level) Computer Vision,” Stanford Computer Science Ph.D. thesis, Artificial Intelligence Memo AIM-204, and Computer Science Report No. 266, May 1973. [185] 32. Yoshiaki Shirai, “A Heterarchical Program for Recognition of Polyhedra,” MIT AI Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 187
- 188. 9 NOTES Memo No. 263, June 1972. Available online at ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-263.pdf. [185] 33. Alan K. Mackworth, “Interpreting Pictures of Polyhedral Scenes,” Artificial Intelligence, Vol. 4, No. 2, pp. 121–137, June 1973. [185] 188 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 189. 10.1 Chapter 10 “Hand–Eye” Research The motivation for much of the computer vision research that I have described during this period was to provide information to guide a robot arm. Because the images that could be analyzed best were of simple objects such as toy blocks, work was concentrated on getting a robot arm to stack and unstack blocks. I’ll describe some typical examples of this “hand–eye” research, beginning with a project that did not actually involve an “eye.” 10.1 At MIT A computer-guided mechanical “hand” was developed by Heinrich A. Ernst in 1961 as part of his Electrical Engineering Sc.D. work at MIT.1 (His advisor was Claude Shannon.) The hand, named MH-1, was a “mechanical servomanipulator [an American Machine and Foundry model 8] adapted for operation by the TX-0 computer.” It used tactile sensors mounted on the hand to guide it because, as Ernst wrote, “organs for vision are too difficult to build at the present time.” The abstract of Ernst’s thesis describes some of what the system could do: [O]ne program consisting of nine statements will make the hand do the following: Search the table for a box, remember its position, search the table for blocks, take them and put them into the box. The position of the objects is irrelevant as long as they are on the table. If as a test for the built-in mechanical intelligence, the box should be taken away and placed somewhere else while the hand searches for blocks, MH-1 will remember the new position of the box and continue to work with it as soon as it has realized the change in the situation, that is, has bumped into the box while looking for blocks. This will be done automatically, without any Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 189
- 190. 10 “Hand–Eye” Research need to mention it in the specific program for this block-and-box performance. Actually, MH-1 was not the first computer-guided hand, although it was the first to employ touch sensors to guide its motion. One was developed and patented in 1954 by George Devol, an American engineer. Based on this invention, he and another engineer, Joseph F. Engelberger, founded Unimation, Inc. Soon after, they installed a prototype of their first industrial robot, called a “Unimate,” in the General Motors Corporation Ternstedt Division plant near Trenton, New Jersey. Back at MIT, Ph.D. students Patrick Winston (later an MIT professor and director of its AI Laboratory), Thomas O. Binford (later a Stanford professor), Berthold K. P. Horn (later an MIT professor), and Eugene Freuder (later a University of New Hampshire professor) developed a system that used an AMF Versatran robot arm to “copy” a configuration of blocks. The scene consisting of the blocks was first scanned, and lines were extracted from the image using a “line-finder,” which was under development by Binford and Horn.2 Using these lines, objects in the image were identified, and a plan was made for the robot arm to disassemble the blocks in the scene. The robot arm then carried out this plan and reassembled the blocks in their original configuration. The system was demonstrated in December 1970 for various configurations of blocks. An example block configuration successfully handled by their “copy demo” is shown in Fig. 10.1. (A film, called Eye of the Robot, showing the copy demo in action is available at http://projects.csail.mit.edu/films/aifilms/digitalFilms/9mpeg/88-eye.mpg.) The system depended on precise illumination and carefully constructed blocks. Attempts to extend the range of computer vision to less constrained scenes led to further concentration at MIT and elsewhere on the early stages of vision processing. I’ll describe some of the ensuing work on these problems in more detail later.3 10.2 At Stanford Meanwhile at John McCarthy’s SAIL, a team led by Professor Jerome Feldman (1938– ) pursued work on hand–eye projects using the PDP-1 and later the PDP-6 and PDP-10 computers.4 McCarthy later told me that he got interested in robots because of his interest in computer vision. He was not very excited about the work in pattern recognition – it was “discrimination” rather than “description.” “If you want to pick something up, you have to describe it not merely recognize it.”5 In 1966 SAIL had acquired a Rancho Los Amigos Hospital electromechanical prosthetic arm. By the spring of 1967, a hand–eye system was developed by Karl Pingle, Jonathan Singer, and Bill Wichman that could 190 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 191. 10.2 At Stanford Figure 10.1: A block arrangement for the MIT copy demo. (Used with permis- sion of Berthold Horn.) use a TV camera and primitive vision routines to locate blocks scattered on a table. Using the information thus obtained, it could control the arm to sort the blocks.6 According to the authors, One section of the system scans the TV image to find the outer edge of an object, then traces around the outside edges of the object using a gradient operator [an edge detector] to find the location and direction of the edge. Curve fitting routines fit straight lines to a list of points found on the edges and calculate the position of the corners. . . . A second section of the system is devoted to control of the arm. . . . The sections which control it consist of a solution program which calculates the angular position required at each actuator and a servo program which drives the arm to the desired positions. . . Les Earnest wrote that this was “the first robotics visual feedback system,” although “only the outer edges of the blocks were observed, [and] the Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 191
- 192. 10 “Hand–Eye” Research hand had to be removed from view when visual checking was done. . . .”7 Several versions of this block-sorting and stacking system were demonstrated. In one, the system located colored blocks on a table and placed them in separate stacks of red and blue blocks.8 By 1971, a vision-guided block stacking system solved the “instant insanity” puzzle.9 In that puzzle, four cubes, each face of which has one of four different colors, must be arranged in a tower such that each side of the tower shows four different colors. The system, running on SAIL’s PDP-10 computer, used a TV camera equipped with a turret of four lenses and a color wheel to locate four cubes on a table top. The arm picked up and turned each cube to expose all faces to the camera. Then, knowing the color of each face and having found a solution to the puzzle, the arm stacked the cubes in a tower exhibiting the solution. [A silent, 16-mm color six-minute film, titled Instant Insanity, was made by Richard Paul and Karl Pingle in August 1971 and shown at the second International Joint Conference on Artificial Intelligence (IJCAI) in London. The film can be seen at http://www.youtube.com/watch?v=O1oJzUSITeY. ] Dabblal Rajagopal “Raj” Reddy (1937– ; Fig. 10.2) was the first Ph.D. student of Stanford’s new Department of Computer Science. His thesis research was on speech recognition. After obtaining his Ph.D. in 1966, Reddy joined Stanford’s faculty and continued research on speech recognition at SAIL. While there he participated in a project to control a hand–eye system by voice commands.10 As stated in a project review, “Commands as complicated as ‘Pick up the small block in the lower lefthand corner,’ are recognized and the tasks are carried out by the computer controlled arm.” (The system was demonstrated in a 1969 fifteen-minute, 16-mm color, sound film showing some of Reddy’s results on speech recognition. It is titled Hear Here and was produced by Raj Reddy, Dave Espar, and Art Eisensen. The film is available at http://www.archive.org/details/sailfilm hear.) In 1969 Reddy moved to CMU where he pursued research in speech recognition and later became Dean of CMU’s School of Computer Science. In the early 1970s, the Stanford team used a vision system and a new electromechanical hand designed by mechanical engineering student Vic Scheinman11 to assemble a model “T” Ford racing water pump.12 An industrial-style setup was used with tools in fixed places, screws in a feeder, and the pump body and cover on a pallet. A diagram of the workspace is shown in Fig. 10.3. The hand–eye system executed the following complex set of steps that was computed previously: locate the pump base, move it into standard position, determine the final grasping position by touch, place the pump base in its standard position, insert two pins to guide the alignment of the gasket and cover, put on the gasket, visually check the position of 192 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 193. 10.4 In Japan Figure 10.2: Raj Reddy. (Photograph courtesy of Raj Reddy.) the gasket, locate the cover by touch, put on the cover over the guide pins, pick up a hex head power screw driver, pick up a screw from the feeder, screw in the first two screws, remove the aligning pins, screw in the last four screws, and finally check the force required to turn the rotor. A film of the water pump assembly can be seen at http://www.archive.org/details/sailfilm pump. It is also available at http://www.saildart.org/films/, along with several other Stanford AI Lab films. 10.3 In Japan Hand–eye work was also being pursued at Hitachi’s Central Research Laboratory in Tokyo. There, Masakazu Ejiri and colleagues developed a robot system called HIVIP consisting of three subsystems called EYE, BRAIN, and HAND. (See Fig. 10.4.) One of EYE’s two television cameras looked at a plan drawing depicting an assembly of blocks. The other camera looked at some blocks on a table. Then, BRAIN figured out how to pick up and assemble the blocks as specified in the drawing, and HAND did the assembly.13 10.4 Edinburgh’s “FREDDY” During the late 1960s and into the 1970s, researchers under the direction of Professor Donald Michie in the Department of Machine Intelligence and Perception at the University of Edinburgh developed robot systems generally Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 193
- 194. 10 “Hand–Eye” Research Figure 10.3: Diagram of a water pump assembly workspace. (Illustration used with permission of Robert Bolles.) called “FREDDY.”14 The best known of these was the hand–eye system FREDDY II, which had a large robot arm and two TV cameras suspended over a moving table. Even though the arm did not move relative to the room, it did relative to its “world,” the table. The setup is shown in Fig. 10.5. A demonstration task for FREDDY II was to construct complete assemblies, such as a toy car or boat, from a kit of parts dumped onto the table. The aim was to develop AI techniques that could provide the basis for better industrial assembly robots, that is, robots that were more versatile, more reliable, and more easily programmed than those in operation at that time. At the beginning, the component parts were in an unorganized jumble. FREDDY had to find and identify them and then lay them out neatly. Once it had found all the parts needed, FREDDY could then perform the assembly sequence, using a small workstation with a vice. 194 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 195. 10.4 Edinburgh’s “FREDDY” Figure 10.4: Hitachi’s HIVIP robotic assembly system. Isolated parts were recognized from features of their outline (corners, curves, etc.), their holes, and their general properties. These were taught to FREDDY by showing it different views of each of the parts in a prior training phase. To deal with heaps of parts, FREDDY applied several tactics: It could try to find something protruding from the heap, which it could grasp and pull out; it could attempt to lift something (unknown) off the top; or it could simply plough the hand through the heap to try to split it into two smaller ones. Constructing the assembly was performed by following a sequence of instructions that had been programmed interactively during the training phase. Some instructions were simple movements, but others were much more sophisticated and used the force sensors in the hand. For example, in a “constrained move,” the hand would slide the part it held along a surface until it hit resistance; in “hole fitting,” the hand would fit one part (such as an axle) into a hole in another (such as a wheel) by feel, as humans do. FREDDY could assemble both the car and the boat when the two kits were mixed together and dumped on the table. It took about four hours to do so, primarily because of the limited power of FREDDY’s two computers.15 The main computer was an Elliot 4130 with 64k 24-bit words (later upgraded to 128k) and with a clock speed of 0.5 MHz. It was programmed in the POP-2 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 195
- 196. 10 “Hand–Eye” Research Figure 10.5: FREDDY II, the University of Edinburgh robot. (Photograph courtesy of University of Edinburgh.) language. The robot motors and cameras were controlled by a Honeywell H316, with 4k 16-bit words (later upgraded to 8k words, at a cost of about $8,000 for the additional 8k bytes!). Harry Barrow (1943– ), a key person involved in the work, later gave this account of FREDDY’s operation:16 [A]cquiring an image from the TV camera took quite a few seconds and processing took even longer, and in a single run FREDDY took between 100 and 150 pictures! It took a picture every time it picked up an object to check it has successfully lifted it and not dropped it, and it took a picture every time it put an object down to verify the space was empty. It also scanned the entire world (which required multiple pictures) several times to make a map, 196 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 197. 10.4 NOTES and it looked at each object from two different cameras to do some stereo-style estimation of position and size. In fact, the system made the most intensive use of image data of any robot system in the world. The Stanford and MIT systems only took a very small number of pictures to perform their tasks, and relied heavily on dead reckoning and things not going wrong. We, on the other hand, assumed that things were likely to go wrong (objects dropped, rolling, etc.) and made our system highly robust. I really believe that in many ways it was probably the most advanced hand–eye system in existence at the time. FREDDY is now on permanent exhibition in the Royal Scottish Museum in Edinburgh, with a continuous-loop movie of FREDDY assembling the mixed model car and boat kits. Hand–eye research at Edinburgh was suspended during the mid-1970s in part owing to an unfavorable assessment of its prospects in a study commissioned by the British Science Research Council. (I’ll have more to say about that assessment later.) Notes 1. Heinrich A. Ernst, “MH-1, A Computer-Operated Mechanical Hand,” Sc.D. thesis, Massachusetts Institute of Technology, Department of Electrical Engineering, 1962. Available online at http://dspace.mit.edu/bitstream/handle/1721.1/15735/09275630.pdf?sequence=1. [189] 2. Reported in Berthold K. P. Horn, “The Binford–Horn Line Finder,” MIT AI Memo 285, MIT, July 1971 (revised December 1973). Available online at http://people.csail.mit.edu/bkph/AIM/AIM-285-OPT.pdf. [190] 3. Patrick Winston gives a nice description of the MIT programs just mentioned (including the copy demo and Winston’s own thesis work on learning structural descriptions of object configurations) in Patrick H. Winston, “The MIT Robot,” Machine Intelligence 7, Bernard Meltzer and Donald Michie (eds.), pp. 431–463, New York: John Wiley and Sons, 1972. [190] 4. For a brief review, see Lester Earnest (ed.), “Final Report: The First Ten Years of Artificial Intelligence Research at Stanford,” Stanford Artificial Intelligence Laboratory Memo AIM-228 and Stanford Computer Science Department Report No. STAN-CS-74-409, July 1973. (Available online at http://www-db.stanford.edu/pub/cstr/reports/cs/tr/74/409/CS-TR-74-409.pdf.) For more details see Jerome A. Feldman et al., “The Stanford Hand–Eye Project,” Proceedings of the IJCAI, pp. 521–526, Washington, DC, 1969, and Jerome A. Feldman et al., “The Stanford Hand-Eye Project – Recent Results,” presented at IFIP Congress, Stockholm, 1974. [190] 5. John McCarthy, private communication, August 11, 2007. [190] 6. The system is described in Karl K. Pingle, Jonathan A. Singer, and William M. Wichman, “Computer Control of a Mechanical Arm through Visual Input,” Proceedings of the IFIP Congress (2), pp. 1563–1569, 1968. The vision part of the system is described in William Wichman, “Use of Optical Feedback in the Computer Control of an Arm,” Stanford Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 197
- 198. 10 NOTES Electrical Engineering Department Engineers thesis, August 1967 (and also appears as Stanford Artificial Intelligence Memo AIM-56, 1967.) [191] 7. Lester Earnest, op. cit.. [192] 8. Butterfinger, an 8-minute, 16-mm color film showing a version of this sorting system in operation was produced and directed by Gary Feldman in 1968. The film is available at http://projects.csail.mit.edu/films/aifilms/digitalFilms/1mp4/09-robot.mp4. [192] 9. The system is described in Jerome Feldman et al., “The Use of Vision and Manipulation to Solve the ‘Instant Insanity’ Puzzle,” Proceedings of the IJCAI, pp. 359–364, London: British Computer Society, September 1971. [192] 10. The system is described in Les Earnest et al., “A Computer with Hands, Eyes, and Ears,” Proceedings of the 1968 Fall Joint Computer Conference, Washington, DC: Thompson, 1968. [192] 11. Victor D. Scheinman, “Design of a Computer Manipulator,” Stanford AI Memo AIM-92, June 1, 1969. [192] 12. This task is described in Robert Bolles and Richard Paul, “The Use of Sensory Feedback in a Programmable Assembly System,” Stanford AI Laboratory Memo AIM-220, Stanford Computer Science Department Report STAN-CS-396, October 1973, which is available online at ftp://reports.stanford.edu/pub/cstr/reports/cs/tr/73/396/CS-TR-73-396.pdf. [192] 13. See Masakazu Ejiri et al., “An Intelligent Robot with Cognition and Decision-Making Ability,” Proceedings of the IJCAI, pp. 350–358, London: British Computer Society, September 1971, and Masakazu Ejiri et al., “A Prototype Intelligent Robot That Assembles Objects from Plan Drawings,” IEEE Transactions on Computers, Vol. 21, No. 2, pp. 161–170, February 1972. [193] 14. This is an abbreviation, according to Donald Michie, of Frederick, an acronym of Friendly Robot for Education, Discussion and Entertainment, the Retrieval of Information, and the Collation of Knowledge. [194] 15. http://www.aiai.ed.ac.uk/project/freddy/. The key reference is A. P. Ambler, H. G. Barrow, C. M. Brown, R. M. Burstall, and R. J. Popplestone, “A Versatile Computer-Controlled Assembly System,” Artificial Intelligence, Vol. 6, pp. 129–156, 1975. [195] 16. E-mail note from Harry Barrow of January 3, 2009. [196] 198 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 199. 11.1 Chapter 11 Knowledge Representation and Reasoning For a system to be intelligent, it must have knowledge about its world and the means to draw conclusions from, or at least act on, that knowledge. Humans and machines alike therefore must have ways to represent this needed knowledge in internal structures, whether encoded in protein or silicon. Cognitive scientists and AI researchers distinguish between two main ways in which knowledge is represented: procedural and declarative. In animals, the knowledge needed to perform a skilled action, such as hitting a tennis ball, is called procedural because it is encoded directly in the neural circuits that coordinate and control that specific action. Analogously, automatic landing systems in aircraft contain within their control programs procedural knowledge about flight paths, landing speeds, aircraft dynamics, and so on. In contrast, when we respond to a question, such as “How old are you?,” we answer with a declarative sentence, such as “I am twenty-four years old.” Any knowledge that is most naturally represented by a declarative sentence is called declarative. In AI research (and in computer science generally), procedural knowledge is represented directly in the programs that use that knowledge, whereas declarative knowledge is represented in symbolic structures that are more-or-less separate from the many different programs that might use the information in those structures. Examples of declarative-knowledge symbol structures are those that encode logical statements (such as those McCarthy advocated for representing world knowledge) and those that encode semantic networks (such as those of Raphael or Quillian). Typically, procedural representations, specialized as they are to particular tasks, are more efficient (when performing those tasks), whereas declarative ones, which can be used by a variety of different programs, are more generally useful. In this chapter, I’ll describe some of the ideas put forward during this period for reasoning with and for representing declarative knowledge. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 199
- 200. 11 Knowledge Representation and Reasoning 11.1 Deductions in Symbolic Logic Aristotle got things started in logic with his analysis of syllogisms. In the nineteenth century, George Boole developed the foundations of propositional logic, and Gottlob Frege improved the expressive power of logic by proposing a language that could include internal components (called “terms”) as part of propositions. Later developments by various logicians gave us what we call today the predicate calculus – the very language in which McCarthy proposed to represent the knowledge needed by an intelligent system. Here is an instance of one of Aristotle’s syllogisms, stated in the language of the predicate calculus: 1. (∀x)[Man(x) ⊃ Mortal(x)] (The expression “(∀x)” is a way of writing “for all x”; and the expression “⊃” is a way of writing “implies that.” “Man(x)” is a way of writing “x is a man”; and “Mortal(x)” is a way of writing “x is mortal.” Thus, the entire expression is a way of writing “for all x, x is a man implies that x is mortal” or, equivalently, “all men are mortal.”) 2. Man(Socrates) (Socrates is a man.) 3. Therefore, Mortal(Socrates) (Socrates is mortal.) Statement 3, following “Therefore,” is an example of a deduction in logic. McCarthy proposed that the knowledge that an intelligent agent might need in a specific situation could be deduced from the general knowledge given to it earlier. Thus, for McCarthy-style AI, not only do we need a language (perhaps that of the predicate calculus) but a way to make the necessary deductions from statements in the language. Logicians have worked out a variety of deduction methods based on what they call “rules of inference.” For example, one important inference rule is called modus ponens (Latin for “mode that affirms”). It states that if we have the two logical statements P and P ⊃ Q, then we are justified in deducing the statement Q. By the 1960s programs had been written that could use inference rules to prove theorems in the predicate calculus. Chief among these were those of Paul Gilmore at IBM,1 Hao Wang at IBM,2 and Dag Prawitz,3 now at Stockholm University. Although their programs could prove simple theorems, proving more complex ones would have required too much search.4 A Harvard Ph.D. student, Fisher Black (1938–1995), later a co-inventor of the Black–Scholes equation for pricing options,5 had done early work 200 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 201. 11.1 Deductions in Symbolic Logic implementing some of McCarthy’s ideas.6 But it was a Stanford Ph.D. student and SRI researcher, C. Cordell Green, who programmed a system, QA3, that more fully realized McCarthy’s recommendation. Although it was not difficult to represent world knowledge as logical statements, what was lacking at the time of Black’s work was an efficient mechanical method to deduce conclusions from these statements. Green was able to employ a new method for efficient reasoning developed by John Alan Robinson. During the early 1960s, the English (and American) mathematician and logician John Alan Robinson (1930– ) developed a deduction method particularly well suited to computer implementation. It was based on an inference rule he called “resolution.”7 Although a full description of resolution would involve too much technical detail, it is a rule (as modus ponens is) whose application produces a new statement from two other statements. For example, resolution applied to the two statements ¬P ∨ Q and P produces Q. (The symbol “¬” is a way of writing “not,” and the symbol “∨” is a way of writing “or.”). Resolution can be thought of as canceling out the P and the ¬P in the two statements. (Resolution is a kind of generalized modus ponens as can be seen from the fact that ¬P ∨ Q is logically equivalent to P ⊃ Q.) This example was particularly simple because the statements had no internal terms. Robinson’s key contribution was to show how resolution could be applied to general expressions in the predicate calculus, expressions such as ¬P(x) ∨ Q(x) with internal terms. The advantage of resolution is that it can be readily implemented in programs to make deductions from a set of logical statements. To do so, the statements must first be converted to a special form consisting of what logicians call “clauses.” (Loosely speaking, a clause is a formula that uses only ∨’s and ¬’s.) Any logical statement can be converted to clause form (although some, such as John McCarthy, complain that conversion might eliminate clues about how statements might best be used in logical deductions). The first use of resolution was in computer programs to prove mathematical theorems. (Technically, a “theorem” is any logical statement obtained by successively applying a rule of inference, such as resolution, to members of a base set of logical statements, called “axioms,” and to statements deduced from the axioms.) Groups at Argonne National Laboratories (under Lawrence Wos), at the University of Texas at Austin (under Woody Bledsoe), and at the University of Edinburgh (under Bernard Meltzer) soon began work developing theorem-proving programs based on resolution. These programs were able to prove theorems that had previously been proved “by hand” and even some new, never-before-proved, mathematical theorems.8 One of these latter concerned a conjecture by Herbert Robbins that a Robbins algebra was Boolean. The conjecture was proved in 1996 by William McCune, using an automated theorem prover.9 Our concern here, though, is with using deduction methods to automate the reasoning needed by intelligent systems. Around 1968, Green (aided by Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 201
- 202. 11 Knowledge Representation and Reasoning another Stanford student, Robert Yates) programmed, in LISP, a resolution-based deduction system called QA3, which ran on SRI’s time-shared SDS 940 computer. (QA1, Green’s first effort, guided by Bertram Raphael at SRI, was an attempt to improve on Raphael’s earlier SIR system. QA2 was Green’s first system based on resolution, and QA3 was a more sophisticated and practical descendant.) “QA” stood for “question answering,” one of the motivating applications. I’ll present a short illustrative example of QA3’s question-answering ability taken from Green’s Stanford Ph.D. thesis.10 First, two statements are given to the system, namely, 1. ROBOT(Rob) (Rob is a robot.) 2. (∀x)[MACHINE(x) ⊃ ¬ANIMAL(x)] (x is a machine implies that it is not an animal.) The system is then asked “Is everything an animal?” by having it attempt to deduce the statement 3. (∀x)ANIMAL(x) QA3 not only answers “NO,” finding that such a deduction is impossible, but it also gives a “counterexample” as an answer to the question: 4. x = Rob (This indicates that ¬ANIMAL(Rob) contradicts what was to be deduced.) The use of resolution, like that of any inference rule, to deduce some specific conclusion from a large body of logical statements involves the need to decide to which two statements, among the many possibilities, the rule should be applied. Then a similar decision must be made again and again until, one hopes, finally the desired conclusion is obtained. So just as with programs for playing checkers, solving puzzles, and proving geometry theorems, deduction programs are faced with the need to try many possibilities in their search for a solution. As with those other programs, various heuristic search methods have been developed for deduction programs. 11.2 The Situation Calculus Green realized that “question answering” was quite a broad topic. One could ask questions about almost anything. For example, one could ask “What is a 202 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 203. 11.3 Logic Programming program for rearranging a list of numbers so that they are in increasing numerical order?” Or one could ask, “What is the sequence of steps a robot should take to assemble a tower of toy blocks?” The key to applying QA3 to answer questions of this sort lay in using McCarthy’s “situation calculus.” McCarthy proposed a version of logic he called the “situation calculus” in which one could write logical statements that explicitly named the situation in which something or other was true. For example, one toy block may be on top of another in one situation but not in another. Green developed a version of McCarthy’s logic in which the situation, in which something was true, appeared as one of the terms in an expression stating that something was true. For example, to say that block A is on top of block B in some situation S (allowing for the fact that this might not be the case in other situations), Green would write On(A, B, S), to say that block A is blue in all situations, Green would write (∀s)Blue(A, s), and to say that there exists some situation in which block A is on block B, Green would write (∃s)On(A, B, s). Here “(∃s)” is a way of writing “there exists some s such that . . . ” Not only was QA3 able to deduce statements, but when it deduced a so-called existential statement (such as the one just mentioned), it was able to compute an instance of what was alleged to “exist.” Thus, when it deduced the statement (∃s)On(A, B, s), it also computed for which situation the deduction was valid. Green devised a way in which this value could be expressed in terms of a list of actions for a robot that would change some initial situation into the situation for which the deduced statement was true. Thus, for example, QA3 could be used to plan courses of action for a robot. Later, we’ll see how it was used for this purpose. 11.3 Logic Programming In the same way that QA3 could be used to make robot plans, it could also construct simple computer programs. In his 1969 paper, Green wrote The formalization given here [can] be used to precisely state and solve the problem of automatic generation of programs, including recursive programs, along with concurrent generation of proofs of the correctness of these programs. Thus any programs automatically written by this method have no errors. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 203
- 204. 11 Knowledge Representation and Reasoning Figure 11.1: Robert Kowalski (left) and Alain Colmerauer (right). (Photographs courtesy of Robert Kowalski and of Alain Colmerauer.) Green’s work on automatic programming was the first attempt to write programs using logical statements. Around this time, Robert A. Kowalski (1941– ; Fig. 11.1), an American who had just earned a Ph.D. at the University of Edinburgh, and Donald Kuehner developed a more efficient version of Robinson’s resolution method, which they called “SL-resolution.”11 In the summer of 1972, Kowalski visited Alain Colmerauer (1941– ; Fig. 11.1), the head of Groupe d’Intelligence Artificielle (GIA), Centre National de la Recherche Scientifique and Université II of Aix-Marseille in Marseille. Kowalski wrote “It was during that second visit that logic programming, as we commonly understand it, was born.”12 Colmerauer and his Ph.D. student, Philippe Roussel, were the ones who developed, in 1972, the new programming language, PROLOG. (Roussel chose the name as an abbreviation for “PROgrammation en LOGique.”) In PROLOG, programs consist of an ordered sequence of logical statements. The exact order in which these statements are written, along with some other constructs, is the key to efficient program execution. PROLOG uses an ordering based on the ordering of deductions in SL-resolution. Kowalski, Colmerauer, and Roussel all share credit for PROLOG, but Kowalski admits “. . . it is probably fair to say that my own contributions were mainly philosophical and Alain’s were more practical.”13 The PROLOG language gradually grew in importance to rival LISP, although it is used mainly by AI people outside of the United States. Some American researchers, especially those at MIT, argued against PROLOG (and other resolution-based deduction systems), claiming (with some justification) 204 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 205. 11.4 Semantic Networks that computation based on deduction was not efficient. They advocated computation controlled by embedding knowledge about the problem being solved and how best to solve it directly into programs to reduce search. This “procedural embedding of knowledge” was a feature of the PLANNER languages developed by Carl Hewitt and colleagues at MIT. (Hewitt coined the phrase “procedural embedding of knowledge” in a 1971 paper.)14 11.4 Semantic Networks Semantic networks were (and still are) another important format for representing declarative knowledge. I have already mentioned their use by Ross Quillian as a model of human long-term memory. In the 1970s, Stanford cognitive psychologist Gordon Bower (1932– ) and his student John Anderson (1947– ) presented a network-based theory of human memory in their book Human Associative Memory.15 According to a biographical sketch of Anderson, the book “immediately attracted the attention of everyone then working in the field. The book played a major role in establishing propositional semantic networks as the basis for representation in memory and spreading activation through the links in such networks as the basis for retrieval of information from memory.”16 The theory was partially implemented in a computer simulation called HAM (an acronym for Human Associative Memory). HAM could parse simple propositional sentences and store them in a semantic network structure. Using its accumulated memory, HAM could answer simple questions. Several other network-based representations were explored during the late 1960s and early 1970s. Robert F. Simmons, after moving from SDC to the University of Texas in Austin, began using semantic networks as a computational linguistic theory of structures and processing operations required for computer understanding of natural language. He wrote “Semantic nets are simple – even elegant – structures for representing aspects of meaning of English strings in a convenient computational form that supports useful language-processing operations on computers.”17 In 1971, Stuart C. Shapiro (1944– ), then at the University of Wisconsin in Madison, introduced a network structure called MENS (MEmory Net Structure) for storing semantic information.18 An auxiliary system called MENTAL (MEmory Net That Answers and Learns) interacted with a user and with MEMS. MENTAL aided MEMS in deducing new information from that already stored. Shapiro envisioned that MENTAL would be able to answer users’ questions using information stored in MEMS. Shapiro later moved to the State University of New York at Buffalo where he and colleagues are continuing to develop a series of systems called SNePS (Semantic NEtwork Processing System).19 SNePS combines features of logical Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 205
- 206. 11 Knowledge Representation and Reasoning representations with those of network representations and has been used for natural language understanding and generation and other applications.20 In his Ph.D. research in linguistics at the University of Texas at Austin, Roger C. Schank (1946– ; Fig. 11.2) began developing what he called “conceptual dependency representations for natural language sentences.”21 Subsequently, as a Professor at Stanford and at Yale, he and colleagues continued to develop these ideas. The basis of Schank’s work was his belief that people transform natural language sentences into “conceptual structures” that are independent of the particular language in which the sentences were originally expressed. These conceptual structures, he claimed, were how the information in sentences is understood and remembered. So, for example, when one translates a sentence from one language into another, one first represents its information content as a conceptual structure and then uses that structure to reason about what was said or to regenerate the information as a sentence in another language. As he put it in one of his papers, “. . . any two utterances that can be said to mean the same thing, whether they are in the same or different languages, should be characterized in only one way by the conceptual structures.”22 Figure 11.2: Roger Schank. (Photograph courtesy of Roger Schank.) The notation Schank used for his conceptual structures (sometimes called “conceptual dependency graphs”) evolved somewhat during the 1970s.23 As an example, Fig. 11.3, taken from one of his papers, shows how he would represent the sentence “John threw the pencil to Sam.” This structure uses three of the “primitive actions” Schank has defined for these representations. These are ATRANS, which means a transfer of possession; PTRANS, which means a transfer of physical location; and PROPEL, which means an application of force to an object. Schank defined several other primitive actions to represent 206 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 207. 11.5 Scripts and Frames movement, attending to, speaking, transferring of ideas, and so on. An expanded literal reading of what this structure represents would be “John applied physical force to a pencil, which caused it to go through the air from John’s location to Sam’s location, which caused Sam to possess it” or something like that. Schank, like many others who are interested in meaning representation languages, notes that these representations can be used directly to perform deductions and answer questions. For example, answers to questions such as “How did Sam get the pencil?” and “Who owned the pencil after John threw it?” are easily extracted. Although network structures are illustrated graphically in papers about them, they were encoded using LISP for computer processing. Figure 11.3: Conceptual structure for “John threw the pencil to Sam.” (From Roger C. Schank, “Identification of Conceptualizations Underlying Natural Langauge,” in Roger Schank and Kenneth Colby (eds.), Computer Models of Thought and Language, p. 226, San Francisco: W. H. Freeman and Co., 1973.) 11.5 Scripts and Frames Graphical knowledge representations, such as semantic networks and conceptual structures, connect related entities together in groups. Such groupings are efficient computationally because things that are related often participate in the same chain of reasoning. When accessing one such entity it is easy to access close-by ones also. Roger Schank and Robert Abelson expanded on this idea by introducing the concept of “scripts.”24 A script is a way of representing what they call “specific knowledge,” that is, detailed knowledge about a situation or event that “we have been through many times.” They contrast specific knowledge with “general knowledge,” the latter of which is the large body of background or commonsense knowledge that is useful in many situations. Their “restaurant” script (“Coffee Shop version”) became their most Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 207
- 208. 11 Knowledge Representation and Reasoning famous illustrative example. The script consists of four “scenes,” namely, Entering, Ordering, Eating, and Exiting. Its “Props” are Tables, Menu, F-Food, Check, and Money. Its “Roles” are S-Customer, W-Waiter, C-Cook, M-Cashier, and O-Owner. Its “Entry conditions” are S is hungry and S has money. Its “Results” are S has less money, O has more money, S is not hungry, and S is pleased (optional). Figures 11.4 shows their script for the “Ordering” scene. Figure 11.4: A scene in the restaurant script. (From Roger C. Schank and Robert P. Abelson, Scripts, Plans, Goals, and Understanding: An Inquiry into Human Knowledge Structures, p. 43, Hillsdale, NJ: Lawrence Erlbaum Asso- ciates, 1977.) Besides the actions PTRANS (transfer of location) and ATRANS (transfer of possession), this script uses two more of their primitive actions, namely, MTRANS (transfer of information) and MBUILD (creating or combining thoughts). 208 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 209. 11.5 Scripts and Frames CP(S) stands for S’s “conceptual processor” where thought takes place, and DO stands for a “dummy action” defined by what follows. The lines in the diagram show possible alternative paths through the script. So, for example, if the menu is already on the table, the script begins at the upper left-hand corner; otherwise it begins at the upper right-hand corner. I believe most of the script is self-explanatory, but I’ll help out by explaining what goes on in the middle. S brings the “food list” into its central processor where it is able to mentally decide (build) a choice of food. S then transfers information to the waiter to come to the table, which the waiter does. Then, S transfers the information about his or her choice of food to the waiter. This continues until either the cook tells the waiter that he does not have the food that is ordered or the cook prepares the food. The three other scenes in the restaurant script are similarly illustrated in Schank and Abelson’s book.25 Several other variations of the restaurant script (for different kinds of restaurants, and so on) are possible. Scripts help explain some of the reasoning we do automatically when we hear a story. For example, if we hear that John went to a coffee shop and ordered lasagne, we can reasonably assume that lasagne was on the menu. If we later learn that John had to order something else instead, we can assume that the coffee shop was out of lasagne. Schank and Abelson give anecdotal evidence that even small children build such scripts and that people must have a great number of them to enable them to navigate through and reason about situations they encounter. Schank later expanded on scripts and related ideas in another book, in which he introduced the idea of “memory organization packets” (MOPS) that describe situations in a more distributed and dynamic way than scripts do.26 He later “revisited” some of these ideas in a book about their application to education, a field to which he has made significant contributions.27 Schank and his claims generated a good deal of controversy among AI researchers. For example, I remember arguing with him in 1983 in a restaurant somewhere (while waiting for the menu?) about the comparative performance of his programs for natural language understanding and that of our programs at SRI. As I recall, he was eager to make more grandiose claims about what his programs could do than I was prepared to believe or to claim about ours. Tufts University philosopher Daniel Dennett is quoted as having said “I’ve always relished Schank’s role as a gadfly and as a naysayer, a guerrilla in the realm of cognitive science, always asking big questions, always willing to discard his own earlier efforts and say they were radically incomplete for interesting reasons. He’s a gadfly and a good one.”28 I think his basic idea about scripts was prescient. Also, he has produced a great bunch of students. The “AI Genealogy” Web site29 lists almost four dozen Schank students, many of whom have gone on to distinguished careers. Around the time of Schank’s work, Marvin Minsky proposed that knowledge about situations be represented in structures he called “frames.”30 He mentioned Schank’s ideas (among others) as exemplary of a movement Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 209
- 210. 11 NOTES away from “trying to represent knowledge as collections of separate, simple fragments” such as sentences in a logical language. As he defined them, A frame is a data-structure for representing a stereotyped situation, like being in a certain kind of living room, or going to a child’s birthday party. Attached to each frame are several kinds of information. Some of this information is about how to use the frame. Some is about what one can expect to happen next. Some is about what to do if these expectations are not confirmed. . . . Collections of related frames are linked together into frame-systems. The effects of important actions are mirrored by transformations between the frames of a system. These are used to make certain kinds of calculations economical, to represent changes of emphasis and attention, and to account for the effectiveness of “imagery.” Minsky’s paper described how frame systems could be applied to vision and imagery, linguistic and other kinds of understanding, memory acquisition, retrieval of knowledge, and control. Although his paper was rich in ideas, Minsky did not actually implement any frame systems. A couple of years later, some of his students and former students did implement some framelike systems. One, called FRL (for Frame Representation Language), was developed by R. Bruce Roberts and Ira P. Goldstein.31 Daniel Bobrow and Terry Winograd (the latter being one of Papert’s students), implemented a more ambitious system called KRL (for Knowledge Representation Language).32 Frame systems accommodated a style of reasoning in which details “not specifically warranted” could be assumed, thus “bypassing “logic,” as Minsky would have it. This style was already used earlier in Raphael’s SIR system (see p. 134), and researchers advocating the use of logical languages for knowledge representation would later extend logic in various ways to accommodate this style also. Even so, the last section (titled “Criticism of the Logistic Approach”) of Minsky’s paper about frames gives many reasons why one might doubt (along with Minsky) “the feasibility of representing ordinary knowledge effectively in the form of many small, independently ‘true’ propositions.” Notes 1. Paul C. Gilmore, “A Proof Method for Quantification Theory: Its Justification and Realization,” IBM Journal of Research and Development, Vol. 4, pp. 28–35, 1960. [200] 2. Hao Wang, “Proving Theorems by Pattern Recognition,” Communications of the ACM, Vol. 4, No. 3, pp. 229–243, 1960, and Hao Wang, “Toward Mechanical Mathematics,” IBM Journal of Research and Development, Vol. 4, pp. 2–21, 1960. [200] 210 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 211. 11.5 NOTES 3. D. Prawitz, H. Prawitz, and N. Voghera, “A Mechanical Proof Procedure and Its Realization in an Electronic Computer,” Journal of the Association for Computing Machinery, Vol. 7, pp. 102–128, 1960. [200] 4. For additional background and history about automated deduction, see Wolfgang Bibel, “Early History and Perspectives of Automated Deduction,” in J. Hertzberg, M. Beetz, and R. Englert (eds.), Proceedings of the 30th Annual German Conference on Artificial Intelligence (KI-2007), Lecture Notes on Artificial Intelligence, pp. 2–18, Berlin: Springer-Verlag, 2007. [200] 5. In 1997 Myron Scholes and Robert C. Merton were awarded a Nobel Prize in economics for their option-pricing work. Black died of cancer in 1995. The Nobel Prize is not given posthumously; however, in its announcement of the award, the Nobel committee prominently mentioned Black’s key role. [200] 6. Fischer Black, “A Deductive Question-Answering System,” Ph.D. dissertation, Harvard University, June 1964. Reprinted in Marvin Minsky (ed.), Semantic Information Processing, pp. 354–402, Cambridge, MA: MIT Press, 1968. [201] 7. John Alan Robinson, “A Machine-Oriented Logic Based on the Resolution Principle,” Journal of the ACM, Vol. 12, No. 1, pp. 23–41, 1965. [201] 8. For a description of some of this work, see Larry Wos, Ross Overbeek, Ewing Lusk, and Jim Boyle, Automated Reasoning: Introduction and Applications, second edition, New York: McGraw-Hill, 1992. For more recent work, visit Larry Wos’s Web page at http://www.mcs.anl.gov/∼wos/. [201] 9. William McCune, “Solution of the Robbins Problem,” Journal of Automated Reasoning, Vol. 19, No. 3, pp. 263–276, 1997. [201] 10. Available as an SRI Technical Note: C. Green, “Application of Theorem Proving to Problem Solving,” Technical Note 4, AI Center, SRI International, 333 Ravenswood Ave, Menlo Park, CA 94025, March 1969. Online version available at http://www.ai.sri.com/pubs/files/tn004-green69.pdf. See also C. Green, “Theorem Proving by Resolution as a Basis for Question-Answering Systems,” in B. Meltzer and D. Michie, Machine Intelligence 4, pp. 183ff, Edinburgh: Edinburgh University Press, 1969, and C. Green, “Applications of Theorem Proving to Problem Solving,” reprinted from a 1969 IJCAI conference article in B. L. Webber and N. J. Nilsson (eds.), Readings in Artificial Intelligence, pp, 202–222, San Francisco: Morgan Kaufmann, 1981. [202] 11. Robert A. Kowalski and Donald Kuehner, “Linear Resolution with Selection Function,” Artificial Intelligence, Vol. 2, Nos. 3–4, pp. 227–260, 1971. [204] 12. From one of Kowalski’s Web pages: http://www.doc.ic.ac.uk/∼rak/history.html. [204] 13. http://www.doc.ic.ac.uk/∼rak/history.html. For Colmerauer and Roussel’s account of the birth of PROLOG see Alain Colmerauer and Philippe Roussel, “The Birth of PROLOG, in Thomas J. Bergin and Richard G. Gibson (eds.), Programming Languages, New York: ACM Press, Addison-Wesley, 1996. Available online at http://alain.colmerauer.free.fr/ArchivesPublications/HistoireProlog/19november92.pdf. [204] 14. See Carl Hewitt, “Procedural Embedding of Knowledge in PLANNER,” Proceedings of the Second International Joint Conference on Artificial Intelligence, pp. 167–182, Los Altos, CA: Morgan Kaufmann Publishing Co., 1971. [205] 15. John R. Anderson and Gordon H. Bower, Human Associative Memory, Washington, DC: Winston and Sons, 1973. [205] 16. From the Web site http://rumelhartprize.org/john.htm. [205] 17. Robert F. Simmons, “Semantic Networks: Computation and Use for Understanding English Sentences,” in Roger Schank and Kenneth Colby (eds.), Computer Models of Thought and Language, pp. 63–113, San Francisco: W. H. Freeman and Co., 1973. [205] Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 211
- 212. 11 NOTES 18. Stuart C. Shapiro, “A Net Structure for Semantic Information Storage, Deduction and Retrieval,” Proceedings of the Second International Joint Conference on Artificial Intelligence, pp. 512–523, Los Altos, CA: Morgan Kaufmann Publishing Co., 1971. [205] 19. An early paper is Stuart C. Shapiro, “The SNePS Semantic Network Processing System,” in Nicholas V. Findler (ed.), Associative Networks: The Representation and Use of Knowledge by Computers, pp. 179–203, New York: Academic Press, 1979. [205] 20. The SNePS Web page is at http://www.cse.buffalo.edu/sneps/. [206] 21. Roger C. Schank, “A Conceptual Dependency Representation for a Computer-Oriented Semantics,” Ph.D. thesis, University of Texas at Austin, 1969. Available as Stanford AI Memo 83 or Computer Science Technical Note 130, Computer Science Department, Stanford University, Stanford, CA, 1969. [206] 22. Roger C. Schank, “Identification of Conceptualizations Underlying Natural Language,” in Roger Schank and Kenneth Colby (eds.), Computer Models of Thought and Language, pp. 187–247, San Francisco: W. H. Freeman and Co., 1973. [206] 23. Interested readers might refer to various of his books and papers – for example, Roger C. Schank, “Conceptual Dependency: A Theory of Natural Language Understanding” Cognitive Psychology, Vol. 3, pp. 552–631, 1972, and Roger C. Schank, Conceptual Information Processing, New York: Elsevier, 1975. [206] 24. Roger C. Schank and Robert P. Abelson, Scripts, Plans, Goals, and Understanding: An Inquiry into Human Knowledge Structures, Hillsdale, NJ: Lawrence Erlbaum Associates, 1977. [207] 25. Ibid. [209] 26. Roger C. Schank, Dynamic Memory: A Theory of Reminding and Learning in Computers and People, Cambridge: Cambridge University Press, 1982. [209] 27. Roger C. Schank, Dynamic Memory Revisited, Cambridge: Cambridge University Press, 1999. [209] 28. See http://www.edge.org/3rd culture/bios/schank.html. [209] 29. See http://aigp.csres.utexas.edu/∼aigp/researcher/show/192. [209] 30. Marvin Minsky, “A Framework for Representing Knowledge,” MIT AI Laboratory Memo 306, June 1974. Reprinted in Patrick Winston (ed.), The Psychology of Computer Vision, New York: McGraw-Hill, 1975. Available online at http://web.media.mit.edu/∼minsky/papers/Frames/frames.html. [209] 31. R. Bruce Roberts and Ira P. Goldstein, The FRL Primer, Massachusetts Institute of Technology AI Laboratory Technical Report AIM-408, July 1977; available online at ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-408.pdf. [210] 32. Daniel G. Bobrow and Terry A. Winograd, “An overview of KRL, a Knowledge Representation Language,” Report Number CS-TR-76-581, Department of Computer Science, Stanford University, November 1976. Available online at ftp://reports.stanford.edu/pub/cstr/reports/cs/tr/76/581/CS-TR-76-581.pdf. Appeared later as Daniel Bobrow and Terry Winograd, “An Overview of KRL, a Knowledge Representation Language,” Cognitive Science, Vol. 1, No. 1, pp. 3–46, January 1977. [210] 212 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 213. 12.1 Chapter 12 Mobile Robots The hand–eye systems described earlier might be thought of as “robots,” but they could not move about from their fixed base. Up to this time, very little work had been done on mobile robots even though they figured prominently in science fiction. I have already mentioned Grey Walter’s “tortoises,” which were early versions of autonomous mobile robots. In the early 1960s researchers at the Johns Hopkins University Applied Physics Laboratory built a mobile robot they called “The Beast.” (See Fig. 12.1.) Controlled by on-board electronics and guided by sonar sensors, photocells, and a “wallplate-feeling” arm, it could wander the white-walled corridors looking for dark-colored power plugs. Upon finding one, and if its batteries were low, it would plug itself in and recharge its batteries. The system is described in a book by Hans Moravec.1 Beginning in the mid-1960s, several groups began working on mobile robots. These included the AI Labs at SRI and at Stanford. I’ll begin with an extended description of the SRI robot project for it provided the stimulus for the invention and integration of several important AI technologies. 12.1 Shakey, the SRI Robot In November 1963, Charles Rosen, the leader of neural-network research at SRI, wrote a memo in which he proposed development of a mobile “automaton” that would combine the pattern-recognition and memory capabilities of neural networks with higher level AI programs – such as were being developed at MIT, Stanford, CMU, and elsewhere. Rosen had previously attended a summer course at UCLA on LISP given by Bertram Raphael, who was finishing his Ph.D. (on SIR) at MIT. Rosen and I and others in his group immediately began thinking about mobile robots. We also enlisted Marvin Minsky as a consultant to help us. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 213
- 214. 12 Mobile Robots Figure 12.1: The Johns Hopkins “Beast.” (Photograph courtesy of the Johns Hopkins University Applied Physics Laboratory.) Minsky spent two weeks at SRI during August 1964. We made the first of many trips to the ARPA office (in the Pentagon at that time) to generate interest in supporting mobile robot research at SRI. We also talked with Ruth Davis, the director of the Department of Defense Research and Engineering (DDR&E) – the office in charge of all Defense Department research. We wrote 214 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 215. 12.1 Shakey, the SRI Robot a proposal in April 1964 to DDR&E for “Research in Intelligent Automata (Phase I)” that would, we claimed, “ultimately lead to the development of machines that will perform tasks that are presently considered to require human intelligence.”2 The proposal, along with several trips and discussions culminated, in November 1964, in a “work statement” issued by the then-director of ARPA’s Information Processing Techniques Office, Ivan Sutherland. The excerpt in Fig. 12.2 describes the goals of the program.3 Figure 12.2: Excerpt from the typescript of the automaton work statement. In the meantime, Bertram Raphael completed his MIT Ph.D. degree in 1964 and took up a position at UC Berkeley for an academic year. In April 1965, he accepted our offer to join SRI to provide our group with needed AI expertise. After several research proposal drafts and discussions with people in the relevant offices in the Defense Department (complicated by the fact that Ivan Sutherland left ARPA during this time), SRI was finally awarded a rather large (for the time) contract based essentially on Sutherland’s work statement. The “start-work” date on the project, which was administered for ARPA by the Rome Air Development Center (RADC) in Rome, New York, was March Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 215
- 216. 12 Mobile Robots 17, 1966. (Coincidentally, just before joining SRI in 1961, I had just finished a three-year stint of duty as an Air Force Lieutenant at RADC working on statistical signal-processing techniques for radar systems.) Ruth Davis played a prominent role in getting ARPA and RADC to move forward on getting the project started. The “knitting together” of several disparate AI technologies was one of the primary challenges and one of the major contributions of SRI’s automaton project.4 One of the tasks was the actual construction of a robot vehicle whose activities would be controlled by a suite of programs. Because of various engineering idiosyncrasies, the vehicle shook when it came to an abrupt stop. We soon called it “Shakey,” even though one of the researchers thought that sobriquet too disrespectful. [Shakey was inducted into the “Robot Hall of Fame” (along with C-3PO among others) in 2004.5 It was also named as the fifth-best robot ever (out of 50) by Wired Magazine in January 2006. Wired’s numbers 2 and 4 were fictional, “Spirit” and ”Opportunity” (the Mars robots) were number 3, and “Stanley” (winner of the 2005 DARPA “Grand Challenge”) was named “the #1 Robot of All Time.” Shakey is now exhibited at the Computer History Museum in Mountain View, California.]6 Shakey had an on-board television camera for capturing images of its environment, a laser range finder (triangulating, not time-of-flight) for sensing its distance from walls and other objects, and cat-whisker-like bump detectors. Shakey’s environment was a collection of “rooms” connected by doorways but otherwise separated by low walls that we could conveniently see over but Shakey could not. Some of the rooms contained large objects, as shown in Fig. 12.3. The size of Shakey can be discerned from inspection of Fig. 12.4. Most of the programs that we developed to control Shakey were run on a DEC PDP-10 computer. Between the PDP-10 and the mobile vehicle itself were a PDP-15 peripheral computer (for handling the lower level communications and commands to on-board hardware) and a two-way radio and video link. The PDP-10 programs were organized in what we called a “three-layer” hierarchy. Programs in the lowest level drove all of the motors and captured sensory information. Programs in the intermediate level supervised primitive actions, such as moving to a designated position, and also processed visual images from Shakey’s TV camera. Planning more complex actions, requiring the execution of a sequence of intermediate-level actions, was done by programs in the highest level of the hierarchy. The Shakey project involved the integration of several new inventions in search techniques, in robust control of actions, in planning and learning, and in vision. Many of these ideas are widely used today. The next few subsections describe them. 12.1.1 A∗ : A New Heuristic Search Method One of the first problems we considered was how to plan a sequence of “way points” that Shakey could use in navigating from place to place. In getting 216 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 217. 12.1 Shakey, the SRI Robot Figure 12.3: Shakey as it existed in November 1968 (with some of its components labeled). (Photograph courtesy of SRI International.) around a single obstacle lying between its initial position and a goal position, Shakey should first head toward a point near an occluding boundary of the obstacle and then head straight for the unobstructed final goal point. However, the situation becomes more complicated if the environment is littered with several obstacles, and we sought a general solution to this more difficult problem. Shakey kept information about the location of obstacles and about its Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 217
- 218. 12 Mobile Robots Figure 12.4: Charles A. Rosen with Shakey. (Photograph courtesy of SRI In- ternational.) own position in a “grid model,” such as the one shown in Fig. 12.5. (To obtain the required accuracy, grid cells were decomposed into smaller cells near the objects. I think this was one of the first applications of adaptive cell 218 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 219. 12.1 Shakey, the SRI Robot decomposition in robot motion planning and is now a commonly used technique.) Consider, for example, the navigation problem in which Shakey is at position R and needs to travel to G (where R and G are indicated by the shaded squares). It can use a computer representation of the grid model to plan a route before beginning its journey – but how? The map shows the positions of three objects that must be avoided. It is not too difficult to compute the locations of some candidate way points near the corners of the objects. (These way points must be sufficiently far from the corners so that Shakey wouldn’t bump into the objects.) The way points are indicated by shaded stars and labeled “A,” “B,” and so on through “K.” Using techniques now familiar in computer graphics, it also is not difficult to compute which way points are reachable using an obstacle-free, straight-line path from any other way point and from R and G. Figure 12.5: A navigation problem for Shakey. (Illustration used with permis- sion of SRI International.) Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 219
- 220. 12 Mobile Robots Looked at in this way, Shakey’s navigation problem is a search problem, similar to ones I have mentioned earlier. Here is how a search tree can be constructed and then searched for a shortest path from R to G. First, because A and F are directly reachable by obstacle-free, straight-line paths from R, these are set up as direct-descendant “nodes” of R in the search tree. We continue the process of computing descendant nodes (along obstacle-free, straight-line paths) from each of A and F and so on until G is added to the tree. Then, it is a simple matter to identify the shortest path from R to G. Several methods for searching trees (and their more general cousins, graphs) were already in use by the mid-1960s. One point in favor of these known methods was that they were guaranteed to find shortest paths when used to solve Shakey’s navigation problems. However, they could be computationally inefficient for difficult problems. Of course, solving simple navigation problems (such as the one in the diagram) does not involve much search, so any search method would solve such problems quickly. But we were interested in general methods that would work efficiently on larger, more difficult problems. I was familiar with the heuristic search method proposed by J. Doran and Donald Michie for solving the eight-piece, sliding-tile puzzle. They assigned a numerical value to each node in the search tree, based on the estimated difficulty of reaching the goal from that node. The node with the lowest score was the one that was selected next to have its descendants generated.7 I reasoned that a good “heuristic” estimate for the difficulty of getting from a way point position to the goal (before actually searching further) would be the “airline distance,” ignoring any intervening obstacles, from that position to the goal. I suggested that we use that estimate as the score of the corresponding node in the search tree. Bertram Raphael, who was directing work on Shakey at that time, observed that a better value for the score would be the sum of the distance traveled so far from the initial position plus my heuristic estimate of how far the robot had to go.8 Raphael and I described this idea to Peter Hart, who had recently obtained a Ph.D. from Stanford and joined our group at SRI. Hart recalls9 “going home that day, sitting in a particular chair and staring at the wall for more than an hour, and concluding” that if the estimate of remaining distance (whatever it might be) was never larger than the actual remaining distance, then the use of such an estimate in our new scoring scheme would always find a path having the shortest distance to the goal. (Of course, my heuristic airline distance satisfied Hart’s more-general condition.) Furthermore, he thought such a procedure would generate search trees no larger than any other procedures that were also guaranteed to find shortest paths and that used heuristic estimates no better than ours. Together, Hart, Raphael, and I were able to construct proofs for these claims, and we named the resulting search process “A∗ .” (The “A” was for algorithm and the “ ∗ ” denoted its special property of finding shortest paths. 220 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 221. 12.1 Shakey, the SRI Robot I think Hart and Raphael did most of the heavy lifting in devising the proofs.) When paths have costs associated with them that depend on more than just distance, and when such costs (rather than distances) are taken into account in computing scores, A∗ is guaranteed to find lowest cost paths.10 The inclusion of the estimate of remaining distance (or cost) to the goal contributes to searching in the general direction of the goal. The inclusion of the actual distance (or cost) incurred so far ensures that the search process will not forever be led down promising but perhaps futile paths and will be able to “leak around” obstacles. A∗ has been extended in many ways – especially by Richard Korf to make it more practical when computer memory is limited.11 Today, A∗ is used in many applications including natural language parsing,12 the computation of driving directions,13 and interactive computer games.14 12.1.2 Robust Action Execution The A∗ algorithm was embedded in Shakey’s programs for navigating from one place to another within a room containing obstacles and for pushing an object from one place to another. Navigation programs, along with others, occupied the middle level of the hierarchy of Shakey’s programs. These intermediate-level programs were all designed to achieve certain goals, such as getting an object in front of a doorway for example. They were also quite robust in that they “kept trying” even in the face of unforeseen difficulties. For example, if an object being pushed happened accidentally to slip off the front “pushing bar,” the push program noticed this problem (through built-in contact sensors in the pushing bar) and repositioned Shakey so that it could reengage the object and continue pushing. In thinking about how to achieve this robustness, I was inspired both by Miller, Galanter, and Pribram’s TOTE units and by the idea of homeostasis. (Recall that a TOTE unit for driving in a nail keeps pounding until the nail is completely driven in and that homeostatic systems take actions to return them to stability in the face of perceived environmental disturbances.) I wanted the mid-level programs to seek and execute that action that was both “closest” to achieving their goals and that could actually be executed in the current situation. If execution of that action produced a situation in which, as anticipated, an action even closer to achieving the goal could be executed, fine; the mid-level program was at least making progress. If not, or something unexpected caused a setback, some other action would be executed next to get back on track. Richard Duda and I developed a format, called “Markov tables,” for writing these intermediate-level programs having this “keep-trying” property.15 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 221
- 222. 12 Mobile Robots 12.1.3 STRIPS: A New Planning Method The mid-level programs could accomplish a number of simple tasks, such as getting Shakey from one place to another in the same room, pushing objects, and getting Shakey through a doorway into an adjoining room. However, to go to some distant room and push an object there into some designated position would require joining together a sequence of perhaps several of these mid-level programs. Just as humans sometimes make and then execute plans for accomplishing their tasks, we wanted Shakey to be able to assemble a plan of actions and then to execute the plan. The plan would consist of a list of the programs to be executed. Information needed for planning was stored in what was called an “axiom model.” This model contained logical statements in the language of the predicate calculus (which I talked about earlier.) For example, Shakey’s location was represented by a statement such as AT(ROBOT, 7,5), the fact that Box1 was pushable was represented by the statement PUSHABLE(BOX1), and the fact that there was a doorway named D1 between rooms R1 and R2 was represented by the statement JOINSROOMS(D1, R1,R2). The axiom model had close to two-hundred statements such as these and was the basis of Shakey’s reasoning and planning abilities. Our first attempt at constructing plans for Shakey used the QA3 deduction system and the situation calculus. We would ask QA3 to prove (using a version of the axiom model) that there existed a situation in which Shakey’s goal (for example, being in some distant room) was true. The result of the deduction (if successful) would name that situation in terms of a list of mid-level actions to be executed.16 The use of the situation calculus for planning how to assemble mid-level actions involved using logical statements to describe the effects of these actions on situations. Not only did we have to describe how a mid-level action changed certain things about the world, but we also had to state that it left many things unaffected. For example, when Shakey pushed an object, the position of that object in the resulting situation was changed, but the positions of all other objects were not. That most things in Shakey’s world did not change had to be explicitly represented as logical statements and, worse, reasoned about by QA3. This difficulty, called the “frame problem,” has been the subject of a great deal of research in AI, and there have been many attempts to mitigate it, if not solve it.17 Because of the frame problem, QA3 could be used only for putting together the simplest two- or three-step plans. Any attempt to generate plans very much longer would exhaust the computer’s memory. The problem with the situation calculus (as it was used then) was that it assumed that all things might change unless it was explicitly stated that they did not change. I reasoned that a better convention would be to assume that all things remained unchanged unless it was explicitly stated that they did change. To employ a convention like that, I proposed a different way of 222 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 223. 12.1 Shakey, the SRI Robot updating the collection of logical statements describing a situation. The idea was that certain facts, specifically those that held before executing the action but might not hold after, should be deleted and certain new facts, namely, those caused by executing the action, should be added. All other facts (those not slated for deletion) should simply be copied over into the collection describing the new situation. Besides describing the effects of an action in this way, each action description would have a precondition, that is, a statement of what had to be true of a situation to be able to execute the action in that situation. (A year or so earlier, Carl Hewitt, a Ph.D. student at MIT, was developing a robot programming language called PLANNER that had mechanisms for similar kinds of updates.)18 For example, to describe the effects of the program goto((X1,Y1), (X2,Y2)) for moving Shakey from some position (X1,Y1) to some position (X2,Y2), one should delete the logical statement AT(ROBOT, X1,Y1), add the statement AT(ROBOT, X2,Y2), and keep all of the other statements. Of course, to execute goto((X1,Y1), (X2,Y2)), Shakey would already have to be at position (X1,Y1); that is, the axiom model had to contain the precondition statement AT(ROBOT,X1,Y1), or at least contain statements from which AT(ROBOT,X1,Y1) could be proved. Around this time (1969), Richard Fikes (1942– ) had just completed his Ph.D. work under Allen Newell at Carnegie and joined our group at SRI. Fikes’s dissertation explored some new ways to solve problems using procedures rather than using logic as in QA3. Fikes and I worked together on designing a planning system that used preconditions, delete lists, and add lists (all expressed as logical statements) to describe actions. Fikes suggested that in performing a search for a goal-satisfying sequence of actions, the system should use the “means–ends” analysis heuristic central to Newell, Shaw, and Simon’s General Problem Solver (GPS). Using means–ends analysis, search would begin by identifying those actions whose add lists contained statements that helped to establish the goal condition. The preconditions of those actions would be set up as subgoals, and this backward reasoning process would continue until a sequence of actions was finally found that transformed the initial situation into one satisfying the goal. By 1970 or so, Fikes had finished programming (in LISP) our new planning system. We called it STRIPS, an acronym for Stanford Research Institute Problem Solver.19 After its completion, STRIPS replaced QA3 as Shakey’s system for generating plans of action. Typical plans consisting of six or so mid-level actions could be generated on the PDP-10 in around two minutes. The STRIPS planning system itself has given way to more efficient AI planners, but many of them still describe actions in terms of what are called “STRIPS operators” (sometimes “STRIPS rules”) consisting of preconditions, delete lists, and add lists. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 223
- 224. 12 Mobile Robots 12.1.4 Learning and Executing Plans It’s one thing to make a plan and quite another to execute it properly. Also, we wanted to be able to save the plans already made by STRIPS for possible future use. We were able to come up with a structure, called a “triangle table,” for representing plans that was useful not only for executing plans but also for saving them. (John Munson originally suggested grouping the conditions and effects of robot actions in a triangular table. Around 1970, Munson, Richard Fikes, Peter Hart, and I developed the triangle table formalism to represent plans consisting of STRIPS operators.) The triangle table tabulated the preconditions and effects of each action in the plan so that it could keep track of whether or not the plan was being executed properly. Actions in the plans generated by STRIPS had specific values for their parameters. For example, if some goto action was part of a plan, actual place coordinates were used to name the place that Shakey was to go from and the place it was to go to, perhaps goto((3,7),(8,14)). Although we might want to save a plan that had that specific goto as a component, a more generally applicable plan would have a goto component with nonspecific parameters that could be replaced by specific ones depending on the specific goal. That is, we would want to generalize something like goto((3,7),(8,14)), for example, to goto((x1,y1),(x2,y2)). One can’t willy-nilly replace constants by variables, but one must make sure that any such generalizations result in viable and executable plans for all values of the variables. We were able to come up with a procedure that produced correct generalizations, and it was these generalized plans that were represented in the triangle table. After a plan was generated, generalized, and represented in the triangle table, Shakey’s overall executive program, called “PLANEX,” supervised its execution.20 In the environment in which Shakey operated, plan execution would sometimes falter, but PLANEX, using the triangle table, could decide how to get Shakey back on the track toward the original goal. PLANEX gave the same sort of “keep-trying” robustness to plan execution that the Markov tables gave to executing mid-level actions. 12.1.5 Shakey’s Vision Routines Shakey’s environment consisted of the floor it moved about on, the walls bounding its rooms, doorways between the rooms, and large rectilinear objects on the floor in some of the rooms. We made every effort to make “seeing” easy for Shakey. A dark baseboard separated the light-colored floor from the light-colored walls. The objects were painted various shades of red, which appeared dark to the vidicon camera and light to the infrared laser range finder. Even so, visual processing still presented challenging problems. Rather than attempt complete analyses of visual scenes, our work concentrated on using vision to acquire specific information that Shakey 224 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 225. 12.1 Shakey, the SRI Robot needed to perform its tasks. This information included Shakey’s location and the presence and locations of objects – the sort of information that was required by the mid-level actions. The visual routines designed to gather that information were embedded in the programs for performing those actions. Known properties of Shakey’s environment were exploited in these routines. Exploiting the fact that the objects, the floor, and the wall contained planes of rather constant illumination, Claude Brice and Claude Fennema in our group developed image-processing routines that identified regions of uniform intensity in an image.21 Because the illumination on a single plane, say the face of an object, might change gradually over the region, the region-finding routine first identified rather small regions. These were then merged across region boundaries in the image if the intensity change across the boundary was not too great. Eventually, the image would be partitioned into a number of large regions that did a reasonable job of representing the planes in the scene. The boundaries of these regions could then be fitted with straight-line segments. Another vision routine was able to identify straight-line segments in the image directly. Richard Duda and Peter Hart developed a method for doing this based on a modern form of the “Hough transform.”22 After edge-detection processing had identified the locations and directions of small line segments, the Hough transform was used to construct those longer lines that were statistically the most likely, given the small line segments as evidence. Both region finding and line detection were used in various of the vision routines for the mid-level actions. One of these routines, called obloc, was used to refine the location of an object whose location was known only roughly. The pictures in Fig. 12.6 show a box, how it appears as a TV image from Shakey’s camera, and two of the stages of obloc’s processing. From the regions corresponding to the box and the floor (and using the fact that Shakey is on the same floor as the box), straightforward geometric computations could add the box and its location to Shakey’s models. Shakey ordinarily kept track of its location by dead-reckoning (counting wheel revolutions), but this estimate gradually accumulated errors. When Shakey determined that it should update its location, it used another vision routine, called picloc. A nearby “landmark,” such as the corner of a room, was used to update Shakey’s position with respect to the landmark. The pictures in Fig. 12.7 show how obloc traces out the baseboard and finds the regions corresponding to the walls and the floor. The final picture shows the discrepancy between Shakey’s predicted location of the corner (based on Shakey’s estimate of its own location) and the actual location based on picloc. This discrepancy was used to correct Shakey’s estimate of its position. Before Shakey began a straight-line motion in a room where the presence of obstacles might not be known, it used a routine called clearpath to determine whether its path was clear. This routine checked the image of its Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 225
- 226. 12 Mobile Robots Figure 12.6: Using vision to locate an object. (From the film Shakey: An Experiment in Robot Planning and Learning. Used with permission of SRI International.) path on the floor (a trapezoidal-shaped region) for changes in brightness that might indicate the presence of an obstacle. In appraising Shakey’s visual performance, it is important to point out that it was really quite primitive and subject to many errors – even in Shakey’s specially designed environment. As one report acknowledges, “Regions that we wish to keep distinct – such as two walls meeting at a corner – are frequently merged, and fragments of meaningful regions that should be merged are too often kept distinct.” Regarding clearpath, for example, this same report notes that “. . . shadows and reflections can still cause false alarms, and the only solution to some of these problems is to do more thorough scene analysis.”23 Nevertheless, vision played an important part in Shakey’s overall performance, and many of the visual processing techniques developed during the Shakey project are still used (with subsequent improvements) today.24 226 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 227. 12.1 Shakey, the SRI Robot Figure 12.7: Using vision to update position. (From the film Shakey: An Ex- periment in Robot Planning and Learning. Used with permission of SRI Inter- national.) Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 227
- 228. 12 Mobile Robots 12.1.6 Some Experiments with Shakey To illustrate Shakey’s planning and plan-execution and learning methods in action, we set up a task in which Shakey was to push a specified box in front of a specified doorway in a nonadjacent room. To do so, Shakey had to use STRIPS to make a plan to travel to that room and then to push the box. Before beginning its execution of the plan, Shakey saved it in the generalized form described earlier. In the process of executing the plan, we arranged for Shakey to encounter an unexpected obstacle. Illustrating its robust plan execution procedure, Shakey was able to find a different version of the generalized plan that would take it on a somewhat different route to the target room where it could carry on.25 One of the researchers working on the Shakey project was L. Stephen Coles (1941– ), who had recently obtained a Ph.D. degree under Herb Simon at Carnegie Mellon University working on natural language processing. Coles wanted to give Shakey tasks stated in English. He developed a parser and semantic analysis system that translated simple English commands into logical statements for STRIPS. For example, the task of box pushing just mentioned was posed for Shakey in English as follows: Use BOX2 to block door DPDPCLK from room RCLK. (BOX2, DPDPCLK, and RCLK were the names Shakey used to identify the box, door, and room in question. We were obliging enough to use Shakey’s names for things when giving it tasks to perform.) Coles’s program, called ENGROB,26 translated this English command into the following condition to be made true (expressed in the language of the predicate calculus): BLOCKED(DPDPCLK, RCLK, BOX2) This condition was then given to STRIPS to make a plan for achieving it. Coles was also interested in getting Shakey to solve problems requiring indirect reasoning. He set up an experiment in which Shakey was to push a box off an elevated platform. To do so, it would have to figure out that it would need to push a ramp to the platform, roll up the ramp, and then push the box. This task was given to Shakey in English as “Push the box that is on the platform onto the floor.” The task was successfully executed and described in one of the Shakey technical reports.27 The “push-the-box-off-the-platform” task was Coles’s way of showing that Shakey could solve problems like the “monkey-and-bananas” problem. That problem, made famous by John McCarthy as an example for deductive reasoning, involved a monkey, a box, and some bananas hanging out of reach. The monkey was supposed to be able to reason that to get the bananas, it 228 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 229. 12.1 Shakey, the SRI Robot would have to push the box under the bananas, climb up on the box, and then grab the bananas.28 McCarthy is said to have heard Karl Lashley at the 1948 Caltech Hixon symposium describe a similar problem for demonstrating intelligent problem solving by chimpanzees. One of the persons who was impressed with Shakey was Bill Gates, who later co-founded Microsoft. He saw the 1972 Shakey film as a junior in high school and drove down from Seattle to SRI (with Paul Allen, who would be the other co-founder of Microsoft) to have a look. According to one source, he was “particularly excited about Shakey moving things around so it could go up a ramp.”29 12.1.7 Shakey Runs into Funding Troubles Shakey was the first robot system having the abilities to plan, reason, and learn; to perceive its environment using vision, range-finding, and touch sensors; and to monitor the execution of its plans. It was, perhaps, a bit ahead of its time. Much more research (and progress in computer technology generally) would be needed before practical applications of robots with abilities such as these would be feasible. We mentioned some of the limiting assumptions that were being made by robot research projects at that time in one of our reports about Shakey: Typically, the problem environment [for the robot] is a dull sort of place in which a single robot is the only agent of change – even time stands still until the robot moves. The robot itself is easily confused; it cannot be given a second problem until it finishes the first, even though the two problems may be related in some intimate way. Finally, most robot systems cannot yet generate plans containing explicit conditional statements or loops. Even though the SRI researchers had grand plans for continuing work on Shakey, DARPA demurred, and the project ended in 1972. This termination was unfortunate, because work on planning, vision, learning, and their integration in robot systems had achieved a great deal of momentum and enthusiasm among SRI researchers. Furthermore, several new ideas for planning and visual perception were being investigated. Many of these were described in detail in a final report for the Shakey project.30 Among these ideas, a particularly important one involved techniques for constructing plans in a hierarchical fashion. To do so, an overall plan consisting of just “high-level” actions must be composed first. Such a plan can be found with much less searching than one consisting of all of the lowest level actions needed. For example, one’s plan for getting to work might involve only the decision either to take the subway or to drive one’s car. Then, gradually, the high-level plan must be refined in more and more detail until actions at Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 229
- 230. 12 Mobile Robots the lowest level (such as which set of car keys should be used) would eventually be filled in. A Stanford computer science graduate student working at SRI, Earl Sacerdoti (1948– ), proposed two novel methods for hierarchical planning. First (as part of his master’s degree work), he programmed a system he called ABSTRIPS.31 It consisted of a series of applications of STRIPS – beginning with an easy-to-compose plan that ignored all but the most important operator preconditions. Subsequent applications of STRIPS, guided by the higher level plans already produced, would then gradually take the more detailed preconditions into account. The result was a series of ever-more-detailed plans, culminating in one that could actually be executed. For his Ph.D. work, Sacerdoti went on to develop a more powerful hierarchical planning system he called NOAH (for Nets of Action Hierarchies).32 Unlike ABSTRIPS, whose action operators were all at the same level of detail (albeit with preconditions that could be selectively ignored), NOAH employed action operators at several levels of detail. Each operator came equipped with specifications for how it could be elaborated by operators at a lower level of detail. Furthermore, NOAH’s representation of a plan, in a form Sacerdoti called a “procedural network,” allowed indeterminacy about the order in which plan steps at one level might be carried out. This “delayed commitment” about ordering permitted the more detailed steps of the elaborations of nonordered plans at one level to be interleaved at the level below, often with a consequent improvement in overall efficiency. Sacerdoti was hoping to use his hierarchical planning ideas in the Shakey project, so he and the rest of us at SRI were quite disappointed that DARPA was not going to support a follow-on project. (Basic research on robots was one of the casualties of the DARPA emphasis on applications work that began in the early 1970s.) However, we were able to talk DARPA into a project that had obvious military relevance but still allowed us to continue work on automatic planning, vision, and plan execution. Interestingly, the project was pretty much a continuation of our research work on Shakey but with a human carrying out the planned tasks instead of a robot. We called it the “computer-based consultant (CBC) project.” I’ll describe it in a subsequent chapter. Sacerdoti and the SRI researchers were not alone in recognizing the importance of hierarchical planning. As part of his Ph.D. work at the University of Edinburgh, Austin Tate (1951– ) was developing a network-based planning system called INTERPLAN.33 In 1975 and 1976, supported by the British Science Research Council, Tate and colleagues from operations research produced a hierarchical planner called NONLIN.34 The planner took its name from the fact that, like NOAH, some of the plan steps were left unordered until they were elaborated at lower levels of the hierarchy. 230 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 231. 12.2 The Stanford Cart Other planning systems grew out of the NOAH and NONLIN tradition. One was the interactive plan-generation and plan-execution system SIPE-2 developed by David E. Wilkins at SRI International.35 Another was O-PLAN developed by Tate and colleagues at the Artificial Intelligence Applications Institute (AIAI) at the University of Edinburgh.36 These systems have been widely used, extended, and applied.37 12.2 The Stanford Cart In the early 1960s, James Adams, a Mechanical Engineering graduate student at Stanford (and later a Stanford professor), began experimenting with a four-wheeled, mobile cart with a TV camera and a radio control link. Lester Earnest wrote (in his history of the several projects using this cart) “Among other things, Adams showed in his dissertation that with a communication delay corresponding to the round trip to the Moon (about 21 2 seconds) the vehicle could not be reliably controlled if traveling faster than about 0.2 mph (0.3 kph).”38 After Earnest joined the Stanford AI Laboratory, he and Rodney Schmidt, an Electrical Engineering Ph.D. student, got an upgraded version of the cart to “follow a high contrast white line [on the road around the Lab] under controlled lighting conditions at a speed of about 0.8 mph (1.3 kph).” Other AI graduate students also experimented with the cart from time to time during the early 1970s. A picture of the cart (as it appeared around this time) is shown in Fig. 12.8. When Hans Moravec came to Stanford to pursue Ph.D. studies on visual navigation, he began work with the cart, “but suffered a setback in October 1973 when the cart toppled off an exit ramp while under manual control and ended up with battery acid throughout its electronics.” By 1979 Moravec got the refurbished cart, now equipped with stereo vision, to cross a cluttered room without human intervention. But it did this very slowly. According to Moravec,39 The system was reliable for short runs, but slow. The Cart moved 1 m every 10 to 15 min, in lurches. After rolling a meter it stopped, took some pictures, and thought about them for a long time. Then it planned a new path, executed a little of it, and paused again. It successfully drove the Cart through several 20-m courses (each taking about 5 h) complex enough to necessitate three or four avoiding swerves; it failed in other trials in revealing ways. A short video of the cart in action can be seen at http://www.frc.ri.cmu.edu/users/hpm/talks/Cart.1979/Cart.final.mov. Along with Shakey, the Stanford Cart resides in the Computer History Museum in Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 231
- 232. 12 NOTES Figure 12.8: The Stanford cart. (Photograph courtesy of Lester Earnest.) Mountain View, California. They were the progenitors of a long line of robot vehicles, which will be described in subsequent chapters. Notes 1. Hans P. Moravec, Robot: Mere Machine to Transcendent Mind, pp. 18–19, Oxford: Oxford University Press, 1999. [213] 2. A copy of the proposal is available online at http://www.ai.sri.com/pubs/files/1320.pdf. Its cover page says “Prepared by Nils J. Nilsson,” but it was really a team effort, and many of the ideas were elaborations of those put forward in Rosen’s 1963 memo. [215] 3. A copy of the complete statement can be found at http://ai.stanford.edu/∼nilsson/automaton-work-statement.pdf. [215] 4. Online copies of SRI’s proposals for the automaton project, subsequent progress reports, and related papers can be found at http://www.ai.sri.com/shakey/. [216] 5. See http://www.robothalloffame.org/. [216] 6. See http://www.computerhistory.org/timeline/?category=rai. [216] 232 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 233. 12.2 NOTES 7. J. Doran and Donald Michie, “Experiments with the Graph Traverser Program,” Proceedings of the Royal Society of London, Series A, Vol. 294, pp. 235–259, 1966. [220] 8. The first written account of this idea was in Charles A. Rosen and Nils Nilsson, “Application of Intelligent Automata to Reconnaissance,” pp. 21–22, SRI Report, December 1967. Available online at http://www.ai.sri.com/pubs/files/rosen67-p5953-interim3.pdf. [220] 9. Personal communication, October 24, 2006. [220] 10. See Peter Hart, Nils Nilsson, and Bertram Raphael, “A Formal Basis for the Heuristic Determination of Minimum Cost Paths,” IEEE Transactions System Science and Cybernetics, Vol. 4, No. 2, pp. 100–107, 1968, and Peter Hart, Nils Nilsson, and Bertram Raphael, “Correction to ‘A Formal Basis for the Heuristic Determination of Minimum-Cost Paths,”’ SIGART Newsletter, No. 37, pp. 28–29, December 1972. [221] 11. See, for example, Korf’s publications at http://www.cs.ucla.edu/∼korf/publications.html. [221] 12. In a 2003 paper titled “A∗ Parsing: Fast Exact Viterbi Parse Selection,” Dan Klein and Christopher Manning wrote “The use of A∗ search can dramatically reduce the time required to find a best parse by conservatively estimating the probabilities of parse completions.” [221] 13. In an e-mail to Peter Hart dated March 27, 2002, Brian Smart, the chief technical officer of a vehicle-navigation company, wrote “Like most of the ‘location based services’ and ‘vehicle navigation’ industry, we use a variant of A∗ for computing routes for vehicle and pedestrian navigation applications.” [221] 14. In an e-mail to me dated June 14, 2003, Steven Woodcock, a consultant on the use of AI in computer games, wrote “A∗ is far and away the most used. . . and most useful. . . algorithm for pathfinding in games today. At GDC roundtables since 1999, developers have noted that they make more use of A∗ than any other tool for pathfinding.” [221] 15. See Bertram Raphael et al., “Research and Applications – Artificial Intelligence,” pp. 27–32, SRI Report, April 1971. Available online at http://www.ai.sri.com/pubs/files/raphael71-p8973-semi.pdf. [221] 16. For a description of how QA3 developed a plan for Shakey to push three objects to the same place, for example, see Nils J. Nilsson, “Research on Intelligent Automata,” Stanford Research Institute Report 7494, pp. 10ff, February 1969; available online at http://www.ai.sri.com/pubs/files/nilsson69-p7494-interim1.pdf. [222] 17. McCarthy and Hayes first described this problem in John McCarthy and Patrick Hayes, “Some Philosophical Problems from the Standpoint of Artificial Intelligence,” in Donald Michie and Bernard Meltzer (eds.), Machine Intelligence, Vol. 4, pp. 463–502, 1969. Reprinted in Matthew Ginsberg (ed.), Readings in Nonmonotonic Reasoning, pp. 26–45, San Francisco: Morgan Kaufmann Publishers, Inc., 1987. Preprint available online at http://www-formal.stanford.edu/jmc/mcchay69/mcchay69.html. [222] 18. See Carl Hewitt, “ PLANNER: A Language for Proving Theorems in Robots,” Proceedings of the First International Joint Conference on Artificial Intelligence, pp. 295–301, 1969. [223] 19. See Richard Fikes and Nils Nilsson, “STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving,” Artificial Intelligence, Vol. 2, Nos. 3–4, pp. 189–208, 1971. Available online at http://ai.stanford.edu/users/nilsson/OnlinePubs-Nils/PublishedPapers/strips.pdf. [223] 20. The generalization and execution mechanisms are described in Richard Fikes, Peter Hart, and Nils Nilsson, “Learning and Executing Generalized Robot Plans,” Artificial Intelligence, Vol. 3, No. 4, pp. 251–288, 1972. Available online (as an SRI report) at http://www.ai.sri.com/pubs/files/tn070-fikes72.pdf. [224] Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 233
- 234. 12 NOTES 21. See Claude Brice and Claude Fennema, “Scene Analysis Using Regions,” Artificial Intelligence, Vol. 1, No. 3, pp. 205–226, 1970. [225] 22. Richard O. Duda and Peter E. Hart, “Use of the Hough Transformation to Detect Lines and Curves in Pictures,” Communications of the ACM, Vol. 15, pp. 11–15, January 1972. See also, Peter E. Hart, “How the Hough Transform Was Invented,” IEEE Signal Processing Magazine, November, 2009. [225] 23. Bertram Raphael et al., “Research and Applications – Artificial Intelligence,” Part V, SRI Final Report, December 1971; available online at http://www.ai.sri.com/pubs/files/raphael71-p8973-final.pdf. [226] 24. For more information about Shakey’s visual routines, in addition to the final report just cited, see Richard O. Duda, “Some Current Techniques for Scene Analysis,” SRI Artificial Intelligence Group Technical Note 46, October 1970, available online at http://www.ai.sri.com/pubs/files/tn046-duda70.pdf. [226] 25. This experiment, as well as other information about Shakey, is described in Bertram Raphael et al., “Research and Applications – Artificial Intelligence,” SRI Final Report, December 1971, available online at http://www.ai.sri.com/pubs/files/raphael71-p8973-final.pdf; in Nils Nilsson (ed.), “Shakey The Robot,” SRI Technical Note 323, April 1984, available online at http://www.ai.sri.com/pubs/files/629.pdf; and in a 1972 film, Shakey: An Experiment in Robot Planning and Learning, available online at http://www.ai.sri.com/movies/Shakey.ram. [228] 26. L. Stephen Coles, “Talking with a Robot in English,” Proceedings of the International Joint Conference on Artificial Intelligence, Washington, DC, May 7–9, Bedford, MA: The MITRE Corporation, 1969. [228] 27. See L. Stephen Coles et al., “Application of Intelligent Automata to Reconnaissance,” SRI Final Report, November 1969, available online at http://www.ai.sri.com/pubs/files/coles69-p7494-final.pdf. [228] 28. The problem was introduced by McCarthy in his July 1963 memo “Situations, Actions, and Causal Laws,” reprinted as Section 7.2 of his paper “Program with Commonsense,” which appeared in Marvin Minsky (ed.), Semantic Information Processing, pp. 403–418, Cambridge, MA: MIT Press, 1968. [229] 29. E-mail from Eric Horvitz of May 12, 2003. [229] 30. Peter E. Hart et al., “Artificial Intelligence – Research and Applications,” Technical Report, Stanford Research Institute, December 1972. (Available online at http://www.ai.sri.com/pubs/files/hart72-p1530-annual.pdf.) See also Richard E. Fikes, Peter E. Hart, and Nils J. Nilsson, “Some New Directions in Robot Problem Solving,” in Machine Intelligence 7, Bernard Meltzer and Donald Michie (eds.), pp. 405–430, Edinburgh: Edinburgh University Press, 1972. (The SRI Technical Note 68 version is available online at http://www.ai.sri.com/pubs/files/1484.pdf.) [229] 31. Earl D. Sacerdoti, “Planning in a Hierarchy of Abstraction Spaces,” pp. 412–422, Proceedings of the Third International Joint Conference on Artificial Intelligence, 1973. (The SRI AI Center Technical Note 78 version is available online at http://www.ai.sri.com/pubs/files/1501.pdf.) [230] 32. Earl D. Sacerdoti, “The Non-Linear Nature of Plans,” Proceedings of the International Joint Conference on Artificial Intelligence, 1975. (The SRI AI Center Technical Note 101 version is available online at http://www.ai.sri.com/pubs/files/1385.pdf.) Also see Earl D. Sacerdoti, A Structure for Plans and Behavior, New York: Elsevier North-Holland, 1977. (The SRI AI Center Technical Note No. 109 version is available online at http://www.ai.sri.com/pubs/files/762.pdf.) [230] 33. Austin Tate, “Interacting Goals and Their Use,” Proceedings of the Fourth International Joint Conference on Artificial Intelligence (IJCAI-75), pp. 215–218, Tbilisi, USSR, 234 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 235. 12.2 NOTES September 1975; available online at http://www.aiai.ed.ac.uk/project/oplan/documents/ 1990-PRE/1975-ijcai-tate-interacting-goals.pdf. Austin Tate, “Using Goal Structure to Direct Search in a Problem Solver,” Ph.D. thesis, University of Edinburgh, September 1975; available online at http://www.aiai.ed.ac.uk/project/oplan/documents/1990-PRE/. [230] 34. Austin Tate, “Generating Project Networks,” Proceedings of the Fifth International Joint Conference on Artificial Intelligence (IJCAI-77) pp. 888–893, Boston, MA, August 1977; available online at http://www.aiai.ed.ac.uk/project/oplan/documents/1990-PRE/ 1977-ijcai-tate-generating-project-networks.pdf. [230] 35. See the SIPE-2 Web page at http://www.ai.sri.com/∼sipe/. [231] 36. Ken Currie and Austin Tate, “O-PLAN: The Open Planning Architecture,” Artificial Intelligence, Vol. 52, pp. 49–86, 1991. Available online at http://www.aiai.ed.ac.uk/project/oplan/documents/1991/91-aij-oplan-as-published.pdf. [231] 37. See, for example, AIAI’s “Planning and Activity Management” Web page at http://www.aiai.ed.ac.uk/project/plan/. [231] 38. Lester Earnest, “Stanford Cart,” August 2005; available online at http://www.stanford.edu/∼learnest/cart.htm. [231] 39. Hans P. Moravec, “The Stanford Cart and the CMU Rover,” Proceedings of the IEEE, Vol. 71, No. 7, pp. 872–884, July 1983. [231] Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 235
- 236. 12 NOTES 236 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 237. 13.1 Chapter 13 Progress in Natural Language Processing As mentioned previously, the problems of understanding, generating, and translating material in ordinary human (rather than computer) languages fall under the heading of natural language processing. During the “early explorations” phase of AI research, some good beginnings were made on NLP problems. In the subsequent phase, the late 1960s to early 1970s, new work built on these foundations, as I’ll describe in this part of the book. 13.1 Machine Translation W. John Hutchins, who has written extensively about the history of machine translation (MT), has called the period 1967 to 1976, “the quiet decade.”1 Inactivity in the field during this period is due in part to the ALPAC report, which, as I have already said, was pessimistic about the prospects for machine translation. Hutchins claimed “The influence of the ALPAC report was profound. It brought a virtual end to MT research in the USA for over a decade and MT was for many years perceived as a complete failure. . . . The focus of MT activity switched from the United States to Canada and to Europe.”2 One exception to this decade-long lull in the United States was the development of the Systran (System Translator) translating program by Petr Toma, a Hungarian-born computer scientist and linguistics researcher who had worked on the Georgetown Russian-to-English translation system. In 1968, Toma set up a company called Latsec, Inc., in La Jolla, California, to continue the Systran development work he had begun earlier in Germany. The U.S. Air Force gave the company a contract to develop a Russian-to-English translation Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 237
- 238. 13 Progress in Natural Language Processing system. It was tested in early 1969 at the Wright-Patterson Air Force Base in Dayton, Ohio, “where it continues to provide Russian–English translations for the USAF’s Foreign Technology Division to this day.”3 Systran has evolved to be one of the main automatic translation systems. It is marketed by the Imageforce Corporation in Tampa, Florida.4 How well does Systran translate? It all depends on how one wants to measure performance. Margaret Boden mentions two measures, namely, “intelligibility” and “correctness.” Both of these measures depend on human judgement. For the first, one asks “Can the translation be generally understood?” For the second, one asks “Do human ‘post-editors’ need to modify the translation?” Boden states that “in the two-year period from 1976 to 1978, the intelligibility of translations generated by Systran rose from 45 to 78 percent for [raw text input]. . . ” She also notes that human translations score only 98 to 99 percent, not 100 percent. Regarding correctness, Boden states that in 1978 “only 64 percent of the words were left untouched by human post-editors. Even so, human post-editing of a page of Systran output took only twenty minutes in the mid-1980s, whereas normal (fully human) translation would have taken an hour.”5 13.2 Understanding Although the late 1960s and early 1970s might have been a “quiet decade” for machine translation, it was a very active period for other NLP work. Researchers during these years applied much more powerful syntactic, semantic, and inference abilities to the problem of understanding natural language. Typical of the new attitude was the following observation by Terry Winograd, an MIT Ph.D. student during the late 1960s:6 If we really want computers to understand us, we need to give them ability to use more knowledge. In addition to a grammar of the language, they need to have all sorts of knowledge about the subject they are discussing, and they have to use reasoning to combine facts in the right way to understand a sentence and to respond to it. The process of understanding a sentence has to combine grammar, semantics, and reasoning in a very intimate way, calling on each part to help with the others. 13.2.1 SHRDLU Perhaps the NLP achievement that caused the greatest excitement was the SHRDLU natural language dialog system programmed by Terry Winograd (1946– ; Fig. 13.1) for his Ph.D. dissertation (under Seymour Papert) at MIT.7 238 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 239. 13.2 Understanding Figure 13.1: Terry Winograd. (Photograph courtesy of Terry Winograd.) SHRDLU was able to carry on a dialog about what Winograd called a “micro-world,” a world consisting of toy blocks and a “gripper” for moving them about. Unlike the the world of real blocks used in previous MIT and Stanford hand–eye research, Winograd’s blocks world was simulated on a DEC PDP-10 computer and viewed on a DEC 340 black-and-white graphics display such as that shown in Fig. 13.2. SHRDLU could be given commands, typed in ordinary English, to manipulate the blocks using its gripper. It could also be asked questions about the state of its world. It could “understand” those commands and questions, carry out requested manipulations, and ask the user clarifying questions in English about questions or commands it did not understand. Before describing something about SHRDLU’s design, let us look at an example of part of a dialog.8 In the following, the user’s input is numbered and shown in lowercase letters, and SHRDLU’s response is in all caps, Winograd’s commentary follows in the same typeface, and mine is in parentheses in normal type: 1. pick up a big red block. In order to pick up the red block, it had to clear it off by finding a space for the green one and moving the green one away. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 239
- 240. 13 Progress in Natural Language Processing Figure 13.2: SHRDLU’s world. (Illustration used with permission of Terry Wino- grad.) OK. The system answers "OK" when it carries out a command. (I won’t bother to show what the block configuration looks like as the dialog proceeds, assuming you can use your own mental “model” to keep track of things. The gripper is now holding the red block.) 2. grasp the pyramid. There are three pyramids in the scene, so the program does nothing. I DON’T UNDERSTAND WHICH PYRAMID YOU MEAN. 3. find a block which is taller than the one you are holding and put it into the box. 240 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 241. 13.2 Understanding (The user has now apparently changed his mind about pyramid grasping.) The system has to figure out what is meant by "one" and "it," as well as using deduction to decide which block it is holding. BY "IT," I ASSUME YOU MEAN THE BLOCK WHICH IS TALLER THAN THE ONE I AM HOLDING. Printed out to keep us informed. The hand has to set down the red block to pick up the taller blue one. OK. 4. what does the box contain? THE BLUE PYRAMID AND THE BLUE BLOCK. 5. what is the pyramid supported by? Earlier the system responded to "the pyramid" by saying "I don’t understand which pyramid you mean." now, it knows that we must mean the one which was just mentioned. THE BOX This dialog illustrating SHRDLU’s capabilities goes on for forty-two more user inputs and system responses.9 To engage in dialogs like this, SHRDLU needed “knowledge.” It needed to know about the syntax of that part of English it used, and it needed to know what various words and word combinations mean in the context of their use. It also needed to know about its blocks world – how blocks can be manipulated and what it means for an object to be inside of the “box.” It needed to keep track of the dialog so that it could decide to which object mentioned previously a word such as “it” referred. All of this needed knowledge was represented in LISP programs, or “procedures,” as Winograd called them. Knowledge about syntax was represented as a collection of procedures based on the principles of “systemic grammar.”10 Knowledge about the meanings of words in context was represented in procedures that could refer to a dictionary of word meanings, to other parts of the sentence in which the word was used, and to the discourse. Knowledge about the blocks world was represented in two ways: There was a model that gave the locations of all of the objects and there were procedures that could infer the predicted effects (in the model) of manipulations by the gripper on the various objects. The object-moving procedures had information both about the preconditions and about the effects of these manipulations. These procedures were encoded in a version of Hewitt’s PLANNER language, which, as mentioned previously, bore some resemblance to STRIPS operators. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 241
- 242. 13 Progress in Natural Language Processing Additional procedures in the PLANNER language were used for other types of inference needed by the system. Logical rules were expressed as programs, which were capable of making both forward and backward deductions. SHRDLU’s processes for language understanding can be divided into three parts, namely, syntax, semantics, and inference, but doing so is somewhat misleading because the interplay among these parts was a key feature of the system. As Winograd stated, “Since each piece of knowledge can be a procedure, it can call on any other piece of knowledge of any type.” For example, Winograd wrote, “As it finds each piece of the syntactic structure, it checks its semantic interpretation, first to see if it is plausible, then (if possible) to see if it is in accord with the system’s knowledge of the world, both specific and general.” Winograd’s procedural representation of knowledge (together with Hewitt’s PLANNER language for encoding such representations) can be contrasted with McCarthy’s use of logical formulas to represent knowledge declaratively. The success of SHRDLU fueled a debate among AI researchers about the pros and cons of these two knowledge representation strategies – procedural versus declarative. Actually, the use of LISP to represent procedures blurs this distinction to some extent because, as Winograd pointed out, “LISP allows us to treat programs as data and data as programs.” So, even though SHRDLU’s knowledge was represented procedurally, it was able to incorporate some declarative new knowledge (presented to it as English sentences) into its procedures. SHRDLU’s performance was indeed quite impressive and made some natural language researchers optimistic about future success.11 However, Winograd soon abandoned this line of research in favor of pursuing work devoted to the interaction of computers and people. Perhaps because he had first-hand experience of how much knowledge was required for successful language understanding in something so simple as the blocks world, he despaired of ever giving computers enough knowledge to duplicate the full range of human verbal competence. In a 2004 e-mail, Winograd put SHRDLU’s abilities in context with those of humans:12 There are fundamental gulfs between the way that SHRDLU and its kin operate, and whatever it is that goes on in our brains. I don’t think that current research has made much progress in crossing that gulf, and the relevant science may take decades or more to get to the point where the initial ambitions become realistic. In the meantime AI took on much more doable goals of working in less ambitious niches, or accepting less-than-human results (as in translation). 242 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 243. 13.2 Understanding 13.2.2 LUNAR On their return from the first manned moon landing, the Apollo 11 astronauts brought back several pounds of moon rocks for scientific study. Various data about these rocks were stored in databases that could be accessed by geologists and other scientists. To make retrieval of this information easier for lunar geologists, NASA asked William A. Woods, a young computer scientist at BBN, about the possibility of designing some sort of natural-language “front end” so that the databases could be queried in English instead of in arcane computer code. Woods had just completed his Ph.D. research at Harvard on question-answering systems.13 Sponsored by NASA’s Manned Spacecraft Center, Woods and BBN colleagues Ron Kaplan and Bonnie Webber developed a system they called “LUNAR” for answering questions about the moon rocks.14 LUNAR used both syntactic and semantic processes to transform English questions into moon rock database queries. Syntactic analysis was performed by using “augmented transition networks” (ATNs), a methodology developed by Woods during his Harvard Ph.D. research. (I’ll describe what ATNs are all about shortly.) The semantic component, guided by the ATN-derived parse trees, transformed English sentences into what Woods called a “meaning representation language” (MRL). This language was a logical language (like that of the predicate calculus) but extended with procedures that could be executed. MRL was originally conceived by Woods at Harvard and further developed at BBN. LUNAR was able to “understand” and answer a wide variety of questions, including, for example, “What is the average concentration of aluminum in high alkali rocks?” “How many breccias contain olivine?” “What are they?” (LUNAR recognized that “they” referred to the breccias named as answers to the last question.) LUNAR was the first question-answering system to publish performance data. It was able to answer successfully 78% of the questions put to it by geologists at the Second Annual Lunar Science Conference held in Houston in January 1971. Reportedly, 90% would have been answerable with “minor fixes” to the system. In a June 2006 talk15 about LUNAR, Woods mentioned some of its limitations. The following dialog illustrates one shortcoming: User: What is a breccia? LUNAR: S10018. User: What is S10018? LUNAR: S10018. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 243
- 244. 13 Progress in Natural Language Processing Woods said, “LUNAR simply finds referents of referring expressions and gives their names. There is no model of the purpose behind the user’s question or of different kinds of answers for different purposes.” Although LUNAR could recognize several different ways of phrasing essentially the same question, Woods claimed that “there are other requests which (due to limitations in the current grammar) must be stated in a specific way in order for the grammar to parse them and there are others which are only understood by the semantic interpreter when they are stated in certain ways.”16 13.2.3 Augmented Transition Networks Many people realized that context-free grammars (like the ones I discussed earlier) were too weak for most practical natural language processing applications. For example, if we were to expand the illustrative grammar I described in Section 7.1 so that it included (in addition to “threw” and “hit” and “man”) the present-tense verbs “throw,” “throws,” and “hits” and the plural noun “men,” then the strings “the men hits the ball” and “the man throw the ball” would be inappropriately accepted as grammatical sentences. To expand a context-free grammar to require that nouns and verbs must agree as to number would involve an impractically large collection of rules. Also, allowing for passive sentences, such as “the ball was hit by the men,” would require even further elaboration. Clearly, the sorts of sentences that geologists might ask about moon rocks required more powerful grammars – such as the augmented transition networks that Woods and others had been developing. In Chomsky’s 1957 book17 he had proposed a hierarchy of grammatical systems of which context-free grammars were just one example. His more powerful grammars had a “transformational component” and were able, for example, to parse a sentence such as “the ball was hit by the man” and give it the same “deep structure” as it would give the sentence “the man hit the ball.” Augmented transition network grammars could also perform these kinds of transformations but in a more computationally satisfying way. An augmented transition network is a maplike graphical structure in which the nodes represent points of progress in the parsing process, and the paths connecting two nodes represent syntactic categories. We can think of parsing a sentence as traversing a path through the network from the start node (no progress at all yet) to an end node (where the sentence has been successfully parsed). Traversing the path builds the syntactic structure of the sentence in the form of a parse tree. Analysis of a sentence involves peeling off the words in left-to-right fashion and using them to indicate which path in the network to take. Syntactic analysis could begin by peeling off a single word and finding out from a lexicon whether it was a noun, a determiner, an auxiliary (such as 244 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 245. 13.2 Understanding “does”), an adjective, or some other “terminal” syntactic category. Or it could begin by peeling off a group of words and checking to see whether this group was a noun phrase, a verb phrase, a prepositional phrase, or what have you. In the first case, depending on the category of the single word, we would take a path corresponding to that category leading out from the start node. To accommodate the second case, there would be possible paths corresponding to a noun phrase and the other possible higher level syntactic categories. But how would we decide whether or not we could take the noun-phrase path, for example? The answer proposed by Woods and others was that there would be additional transition networks corresponding to these higher level categories. We would be permitted to take the noun-phrase path in the main transition network only if we could successfully traverse the noun-phrase network. And because one path in the noun-phrase network might start with a prepositional phrase, we would have to check to see whether we could take that path (in the noun-phrase network) by successfully traversing a prepositional-phrase network. This process would continue with one network “calling” other networks in a manner similar to the way in which a program can fire up (or “call”) other programs, possibly recursively. (You will recall my discussion of recursive programs: programs that can call versions of themselves.) For this reason, assemblages of networks like these are called recursive transition networks. The first networks of this kind were developed at the University of Edinburgh in Scotland by James Thorne, Paul Bratley, and Hamish Dewar.18 Later, Dan Bobrow and Bruce Fraser proposed a transition network system that elaborated on the Scottish one.19 Both of these systems also performed auxiliary computations while traversing their networks. These “augmentations” allowed the construction of a “deep structure” representation of the sentence being analyzed. Woods’s work on “augmented, recursive transition networks” built on and refined these ideas and introduced an elegant network definition language.20 As an example, Woods described how one of his networks analyzed the sentence “John was believed to have been shot.”21 After all of the calls to subsidiary networks and all of the auxiliary computations were performed the parse tree shown in Fig. 13.3 was obtained. We can observe two things about this parse tree. First, note the occurrence of “PAST” and “PAST PERFECT” as tense markers. Second, note that the form of the original passive-voice sentence has been transformed to an active-voice sentence using a presumed pronoun “SOMEONE.” As Woods notes, the structure can be paraphrased as “Someone believed that someone had shot John.” Network grammars get at the “deep structure” of sentences by transforming then into a standard form. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 245
- 246. 13 Progress in Natural Language Processing Figure 13.3: A parse tree obtained for the sentence “John was believed to have been shot.” (From William A. Woods, “Transition Network Grammars for Natural Language Analysis,” Communications of the ACM, Vol. 13, No. 10, pp. 591–606, 1970.) 13.2.4 GUS Dan Bobrow and Ronald Kaplan, two of the researchers doing NLP work at BBN, moved to the newly formed Palo Alto Research Center (PARC) of Xerox in the mid 1970s. One of the projects that they and other researchers worked on there was GUS (an acronym for Genial Understander System), which was “intended to engage a sympathetic and highly cooperative human in an English dialog, directed towards a specific goal within a very restricted domain of discourse.”22 The restricted domain was that of a travel agent engaged in “a conversation with a client who wants to make a simple return trip to a single city in California.” The following transcript of an actual dialog shows off some of its abilities: GUS: Hello. My name is GUS. I can help you plan a simple trip by air. Where do you want to go? (1) Client: I want to go to San Diego on May 28. GUS: What time do you want to leave? (2) Client: I must be in San Diego before 10 am. GUS: Would you like P.S.A. flight 102 that arrives at 9.15 am? (3) Client: What is the next flight? GUS: Air California flight 310 that leaves at 8.30 am and arrives at 10.00 am. (4) Client: I’ll take the first one. 246 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 247. 13.2 Understanding GUS: In what name should I make a reservation, please? (5) Client: Dan Bobrow. GUS: I have confirmed the following flight: P.SA. flight 102 on Wednesday May 28 from San Jose to San Diego that leaves at 7.30 am and arrives at 9.15 am. What date do you want to return on? (6) Client: On Friday in the evening. GUS: Would you like the flight that leaves at 7.45 pm? (7) Client: That’s fine. GUS: I have confirmed the following flight: P.S.A. flight 307 on Friday May 30 from San Diego to San Jose that leaves at 7.45 pm and arrives at 9.30 pm. Thank you for calling. Goodbye. Although the abilities of the system were certainly limited, GUS was able to deal with a number of problems. One of these involves what NLP researchers call “resolving anaphora,” by which they mean deciding on the objects or events to which various words or phrases in a dialog refer. Several examples, keyed to the numbered sentences in the dialog above, are mentioned in the paper about GUS: At line (3), for example, the client’s query refers to the flight mentioned in GUS’s immediately preceding utterance. In (4) there is a reference to the flight mentioned earlier in the conversation, [following line (2)]. Note that “next flight” in (3) was to be interpreted relative to the order of flights in the airline guide whereas “first one” in (4) refers to the order in which the flights were mentioned. Another implicit referent underlies the use of “Friday” to specify a date in (6). Resolution of this reference requires some complicated reasoning involving both the content and the context of the conversation. Since May 28 has been given as the departure date, it must presumably be the following Friday that the client has in mind. On the other hand, suppose that the specifications were reversed and Friday had been given as the departure date at line (1). It would then be most readily interpretable as referring to the Friday immediately following the conversation. GUS was a combination of several communicating subsystems, a morphological analyzer for dealing with word components, a syntactic analyzer for generating parse trees, a “reasoner” for figuring out a user’s meanings and intentions, and a language generator for responding. Controlling these components was done by using an “agenda” mechanism. As the authors explain, Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 247
- 248. 13 Progress in Natural Language Processing GUS operates in a cycle in which it examines this agenda, chooses the next job to be done, and does it. In general, the execution of the selected task causes entries for new tasks to be created and placed on the agenda. Output text generation can be prompted by reasoning processes at any time, and inputs from the client are handled whenever they come in. There are places at which information from a later stage (such as one involving semantics) are fed back to an earlier stage (such as the parser). A supervisory process can reorder the agenda at any time. The syntactic component of GUS had “access to a main dictionary of more than 3,000 stems and simple idioms.” The syntactic analyzer was based on a system developed earlier by Ronald Kaplan, which used a transition-network grammar and was called a “General Syntactic Processor.”23 Client sentences were encoded in “frames” (which are related to Minsky’s frames but closer in form to semantic networks). Some frames described the sequence of a normal dialog, whereas others represented the attributes of a date, a trip plan, or a traveler. GUS’s reasoning component used the content and structure of the frames to deduce how best to interpret client sentences. Besides anaphora, the paper mentioned several other problems that GUS was able to deal with. However, it also cautioned that “it is much too easy to extrapolate from [the sample dialog] a mistaken notion that GUS contained solutions to far more problems than it did.” Sample dialogs recorded between human clients and humans playing the role of a GUS revealed numerous instances in which the computer GUS would fail. The authors concluded that if users of systems like GUS departed “from the behavior expected of them in the minutest detail, or if apparently insignificant adjustments are made in their structure,” the systems would act as if they had “gross aphasia” or had just simply died. The authors conceded that “GUS itself is not very intelligent, but it does illustrate what we believe to be essential components of [an intelligent language understanding] system. . . . [It] must have a high quality parser, a reasoning component, and a well structured data base of knowledge.” Subsequent work on NLP at PARC and many other places sought to improve all of these components. The systems developed by researchers such as Winograd, Woods, Bobrow, and their colleagues were very impressive steps toward conversing with computers in English. Yet, there was still a long way to go before natural language understanding systems could perform in a way envisioned by Winograd in the preface to his Ph.D. dissertation: Let us envision a new way of using computers so they can take instructions in a way suited to their jobs. We will talk to them just as we talk to a research assistant, librarian, or secretary, and they will carry out our commands and provide us with the information we ask for. If our instructions aren’t clear enough, they will ask for 248 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 249. 13.2 NOTES more information before they do what we want, and this dialog will all be in English. Notes 1. W. John Hutchins, “Machine Translation: A Brief History,” in E. F. K. Koerner and R. E. Asher (eds.), Concise History of the Language Sciences: From the Sumerians to the Cognitivists, pp. 431–445, Oxford: Pergamon Press, 1995. (Also available online at http://ourworld.compuserve.com/homepages/WJHutchins/Conchist.htm.) [237] 2. Ibid. [237] 3. W. John Hutchins, Machine Translation: Past, Present, Future, Chichester: Ellis Horwood, 1986. An updated (2003) version is available online at http://www.hutchinsweb.me.uk/PPF-TOC.htm. Some of the technical details of Systran’s operation are described in the book. [238] 4. http://www.translationsoftware4u.com/. [238] 5. Margaret A. Boden, Mind as Machine: A History of Cognitive Science, p. 683, Oxford: Oxford University Press, 2006. [238] 6. This quote is from the preface of Winograd’s Ph.D. dissertation. [238] 7. SHRDLU is described in Winograd’s dissertation “Procedures as a Representation for Data in a Computer Program for Understanding Natural Language.” It was issued as an MIT AI Technical Report No. 235, February 1971, and is available online at https://dspace.mit.edu/bitstream/1721.1/7095/2/AITR-235.pdf. The thesis was also published as a full issue of Cognitive Psychology, Vol. 3, No. 1, 1972, and as a book Understanding Natural Language, New York: Academic Press, 1972. The letters in SHRDLU comprise the second column of keys in linotype machines, which were used to set type before computers were used for that. This nonsense word was often used in MAD magazine, which Winograd read in his youth. Failing to think of an acceptable acronym to use to name his system, Winograd used SHRDLU. For Winograd’s account, see http://hci.stanford.edu/∼winograd/shrdlu/name.html. [238] 8. Taken from Section 1.3 of Winograd’s thesis. [239] 9. Readers interested in the entire dialog can see it either in Winograd’s thesis or on one of his Web sites at http://hci.stanford.edu/∼winograd/shrdlu/. [241] 10. Winograd cites, among others, M. A. K. Halliday, “Categories of the Theory of Grammar,” Word, Vol, 17, No. 3, pp. 241-292, 1961. [241] 11. For a short film of SHRDLU in action, see http://projects.csail.mit.edu/films/aifilms/digitalFilms/3mpeg/26-robot.mpg. [242] 12. From http://www.semaphorecorp.com/misc/shrdlu.html. [242] 13. William A. Woods, “Semantics for a Question-Answering System,” Ph.D. dissertation, Harvard University, August 1967. Reprinted as a volume in the series Outstanding Dissertations in the Computer Sciences, New York: Garland Publishing, 1979. [243] 14. William A. Woods, Ron M. Kaplan, and Bonnie Nash-Webber, “The Lunar Sciences Natural Language Information System: Final Report,” BBN, Cambridge, MA, June 1, 1972. See also William A. Woods, “Progress in Natural Language Understanding – An Application to Lunar Geology,” AFIPS Conference Proceedings, Vol. 42, pp. 441–450, Montvale, New Jersey: AFIPS Press, 1973. [243] 15. See http://www.ils.albany.edu/IQA06/Files/Bill Woods IQA06.pdf. [243] Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 249
- 250. 13 NOTES 16. William A. Woods, “Progress in Natural Language Understanding – An Application to Lunar Geology,” AFIPS Conference Proceedings, Vol. 42, pp. 441–450, Montvale, New Jersey: AFIPS Press, 1973. [244] 17. Noam Chomsky, Syntactic Structures, ’s-Gravenhage: Mouton & Co., 1957. [244] 18. James Thorne, Paul Bratley, and Hamish Dewar, “The Syntactic Analysis of English by Machine,” in D. Michie (ed.), Machine Intelligence 3, pp. 281–309, New York: American Elsevier Publishing Co., 1968; Hamish Dewar, Paul Bratley, and James Thorne, “A Program for the Syntactic Analysis of English Sentences,” Communications of the ACM, Vol. 12, No. 8, pp. 476–479, August 1969. [245] 19. Daniel Bobrow and Bruce Fraser, “An Augmented State Transition Network Analysis Procedure,” Proceedings of the International Joint Conferenece on Artificial Intelligence, pp. 557–567, Washington, DC, 1969. [245] 20. William A. Woods, “Augmented Transition Networks for Natural Language Analysis,” Report CS-1, Aiken Computation Laboratory, Harvard University, Cambridge, MA, December 1969; William A. Woods, “Transition Network Grammars for Natural Language Analysis,” Communications of the ACM, Vol. 13, No. 10, pp. 591–606, 1970. The ACM article has been reprinted in Yoh-Han Pao and George W. Ernest (eds.), Tutorial: Context-Directed Pattern Recognition and Machine Intelligence Techniques for Information Processing, Silver Spring, MD: IEEE Computer Society Press, 1982, and in Barbara Grosz, Karen Sparck Jones, and Bonnie Webber (eds.), Readings in Natural Language Processing, San Mateo, CA: Morgan Kaufmann, 1986. [245] 21. William A. Woods, op. cit., pp. 602ff. [245] 22. Daniel G. Bobrow et al., “GUS, A Frame-Driven Dialog System,” Artificial Intelligence, Vol. 8, pp. 155–173, 1977. [246] 23. Ronald Kaplan, “A General Syntactic Processor,” in R. Rustin (ed.), Natural Language Processing, New York: Algorithmics Press, 1973. [248] 250 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 251. 14.0 Chapter 14 Game Playing I have already mentioned attempts to program computers to play board games, such as chess and checkers. The most successful of these was Arthur Samuel’s checker-playing program. In 1967, Samuel published a paper describing an improved version of his program.1 He had refined the program’s search procedure and incorporated better “book-learning” capabilities, and instead of calculating the estimated value of a position by adding up weighted feature values, he used hierarchically organized tables. According to Richard Sutton, “This version learned to play much better than the 1959 program, though still not at a master level.”2 Between 1959 and 1962, a group of MIT students, advised by John McCarthy, developed a chess-playing program. It was based on earlier programs for the IBM 704 written by McCarthy. One of the group members, Alan Kotok (1941–2006) described the program in his MIT bachelor’s thesis.3 The program was written in a combination of FORTRAN and machine (assembly) code and ran on the IBM 7090 computer at MIT. It used the alpha–beta procedure (as discussed earlier) to avoid generating branches of the search tree that could be eliminated without altering the final result. Kotok claimed that his program did not complete any games but “played four long game fragments in which it played chess comparable to an amateur with about 100 games experience. . . . Most of the machine’s moves are neither brilliant nor stupid. It must be admitted that it occasionally blunders.”4 When McCarthy moved to Stanford, he took the program along with him and continued to work on it. In the meantime, a computer chess program was being developed by Georgi Adelson-Velskiy and colleagues in Alexander Kronrod’s laboratory at the Institute for Theoretical and Experimental Physics (ITEP) in Moscow.5 During a visit to the Soviet Union in 1965, McCarthy accepted a challenge to have the Kotok–McCarthy program play the Soviet program. Beginning on November 22, 1967, and continuing for about nine months, the Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 251
- 252. 14 Game Playing Kotok–McCarthy program (running on a DEC PDP-6 at Stanford) played the Russian program (running on the Russian M-20 computer at ITEP) – the first match to be played by a computer against a computer. In each of the first two games, the Stanford program eked out a draw (by surviving until the agreed-upon limit of 40 moves) against a weak version of the Russian program. However, it lost the last two games against a stronger version of the ITEP program. McCarthy later claimed that, although Stanford had the better computer, ITEP had the better programs.6 The ITEP program was the forerunner of the much improved Kaissa program developed later by Misha Donskoy, Vladimir Arlazarov, and Alexander Ushkov at the Institute of Control Science in Moscow. Richard Greenblatt, an expert programmer at the AI Lab at MIT, thought he could improve on Kotok’s chess program. His work on computer chess eventually led to a program he called MAC HACK VI.7 Being an expert chess player himself, he was able to incorporate a number of excellent heuristics for choosing moves and for evaluating moves in his program. Running on the AI Lab’s DEC PDP-6 and written in efficient machine code, MAC HACK VI was the first program to play in tournaments against human chess players. In an April 1967 tournament, it won two games and drew two, achieving a rating of 1450 on the U.S. Chess Federation rating scale, about the level of an amateur human player. (According to the international rating system for human chess players, the highest level is that of Grand Masters. Then come International Masters, National Masters, Experts, Class A, Class B, and so on. MAC HACK VI played at the high Class C to low Class B level, which is still quite far from master play.) It became an honorary member of the U.S. Chess Federation and of the Massachusetts Chess Association. In a famous match at MIT in 1967,8 Greenblatt’s program beat Hubert Dreyfus (1929– ), an AI critic who had earlier observed that “no chess program could play even amateur chess.”9 Although Dreyfus’s observation was probably true in 1965, Greenblatt’s MAC HACK VI was playing at the amateur level two years later. Perhaps encouraged by MAC HACK’s ability, in 1968 Donald Michie and John McCarthy made a bet of £250 each with David Levy (1945– ), a Scottish International Master, that a computer would be able to beat him within ten years. (The following year Seymour Papert joined in, and in 1971 Ed Kozdrowicki of the University of California at Davis did also, bringing the total bet to £1000. In 1974, Donald Michie raised the total to £1250.) In 1978, Levy collected on his bet – as we shall see later.10 Around 1970, three students at Northwestern University in Illinois, David Slate, Larry Atkin, and Keith Gorlen, began writing a series of chess programs. The first of these, CHESS 3.0, running on a CDC 6400 computer, won the first Association for Computing Machinery’s computer chess tournament (computers against computers) in New York in 1970. There were six entries – MAC HACK VI not among them. According to David Levy, “CHESS 3.0 evaluated approximately 100 positions per second and played at the 1400 252 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 253. 14.0 NOTES level on the U.S. Chess Federation rating scale.” Subsequent Northwestern programs, up through CHESS 4.6, achieved strings of wins at this annual event. Meanwhile, however, CHESS 4.2 was beaten in an early round of the first World Computer Chess Championship tournament held at the International Federation of Information Processing Societies (IFIPS) meeting in Stockholm in 1974. The Russian program, Kaissa, won all four games in that tournament, thereby becoming the world computer chess champion.11 These years, the late 1960s through the mid-1970s, saw computer chess programs gradually improving from beginner-level play to middle-level play. Work on computer chess during the next two decades would ultimately achieve expert-level play, as we shall see in a subsequent chapter. Despite this rapid progress, it was already becoming apparent that there was a great difference between how computers played chess and how humans played chess. As Hans Berliner, a chess expert and a chess programming expert, put it in an article in Nature,12 [A human] uses prodigious amounts of knowledge in the pattern-recognition process [to decide on a good maneuver] and a small amount of calculation to verify the fact that the proposed solution is good in the present instance. . . . However, the computer would make the same maneuver because it found at the end of a very large search that it was the most advantageous way to proceed out of the hundreds of thousands of possibilities it looked at. CHESS 4.6 has to date made several well known maneuvers without having the slightest knowledge of the maneuver, the conditions for its applications, and so on; but only knowing that the end result of the maneuver was good. Berliner summed up the difference by saying that “The basis of human chess strength, by contrast [with computers], is accumulated knowledge” (my italics). Specific knowledge about the problem being solved, as opposed to the use of massive search in solving the problem, came to be a major theme of artificial intelligence research during this period. (Later, however, massive search regained some of its importance.) Perhaps the most influential proponents of the use of knowledge in problem solving were Edward Feigenbaum and his colleagues at Stanford. I’ll turn next to their seminal work. Notes 1. Arthur L. Samuel, “Some Studies in Machine Learning Using the Game of Checkers II – Recent Progress,” IBM Journal of Research and Development, Vol. 11, No. 6, pp. 601–617, 1967. [251] 2. http://www.cs.ualberta.ca/∼sutton/book/11/node3.html. [251] Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 253
- 254. 14 NOTES 3. Alan Kotok, “A Chess Playing Program for the IBM 7090 Computer” MIT bachelor’s thesis in Electrical Engineering, June 1962. Online versions of the thesis are available at http://www.kotok.org/AK-Thesis-1962.pdf and http://www.kotok.org/AI Memo 41.html. (The latter is an MIT memo in which Kotok pointed out that “. . . this report, while written by me, represents joint work of ‘the chess group,’ which consisted of me, Elwyn R. Berlekamp (for the first year), Michael Lieberman, Charles Niessen, and Robert A. Wagner (for the third year). We are all members of the MIT [undergraduate] Class of 1962.) [251] 4. The Computer History Museum has a video “oral history” of Kotok available at http://www.computerhistory.org/chess/alan kotok.oral history highlight.102645440/index. php?iid=orl-433444ecc827d. [251] 5. G. M. Adelson-Velskiy, V. L. Arlazarov, A. R. Bitman, A. A. Zhivotovskii and A.V. Uskov, “Programming a Computer to Play Chess,” Russian Mathematical Surveys 25, March–April 1970, pp. 221–262, London: Cleaver-Hume Press. (Translation of Proceedings of the 1st Summer School on Mathematical Programming, Vol. 2, pp. 216–252, 1969.) [251] 6. See the oral presentation about the history of computer chess at http://video.google.com/videoplay?docid=-1583888480148765375. [252] 7. Richard D. Greenblatt, Donald E. Eastlake III, and Stephen D. Crocker, “The Greenblatt Chess Program,” AI Memo 174, April 1969. Available online at https://dspace.mit.edu/bitstream/1721.1/6176/2/AIM-174.pdf. [252] 8. See an account in the SIGART Newsletter, December 1968. [252] 9. Hubert L. Dreyfus, “Alchemy and Artificial Intelligence,” RAND paper P-3244, p. 10, The RAND Corporation, Santa Monica, CA, December 1965. Available online at http://www.rand.org/pubs/papers/2006/P3244.pdf. [252] 10. For first-hand details about the bet, see David Levy, Robots Unlimited: Life in a Virtual Age, p. 83, Wellesley, MA: A K Peters, Ltd., 2006. [252] 11. See the Computer History Museum’s exhibits on the history of computer chess at http://www.computerhistory.org/chess/index.php. For a concise timeline of computer chess history compiled by Bill Wall, visit http://www.geocities.com/SiliconValley/Lab/7378/comphis.htm. [253] 12. Hans J. Berliner, “Computer Chess,” Nature, Vol. 274, p. 747, August 1978. [253] 254 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 255. 15.0 Chapter 15 The Dendral Project After Ed Feigenbaum moved from UC Berkeley to Stanford in 1965, he became interested in “creating models of the thinking processes of scientists, especially the processes of empirical induction by which hypotheses and theories were inferred from data.” As he put it, “What I needed was a specific task environment in which to study these issues concretely.”1 Feigenbaum recalls attending a Behavioral Sciences workshop at Stanford and hearing a talk by Joshua Lederberg (1925–2008; Fig. 15.1), a Nobel Prize–winning geneticist and founder of the Stanford Department of Genetics. Lederberg talked about the problem of discerning the structure of a chemical compound from knowledge of its atomic constituents and from its mass spectrogram. This sounded like the kind of problem Feigenbaum was looking for, and he and Lederberg soon agreed to collaborate on it.2 Figure 15.1: Edward Feigenbaum (left), Joshua Lederberg (middle), and Bruce Buchanan (right). (Photographs courtesy of Edward Feigenbaum.) Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 255
- 256. 15 The Dendral Project Chemical molecules are described by formulas that give their atomic constituents. For example, the formula for propane is C3H8, indicating that it consists of three carbon atoms and eight hydrogen atoms. But there is more to know about a compound than what atoms it is made of. The atoms composing a molecule are arranged in a geometric structure, and chemists want to know what that structure is. The three carbon atoms in propane, for example, are attached together in a chain. The two carbon atoms at the ends of the chain each have three hydrogen atoms attached to them, and the single carbon atom in the middle of the chain has two hydrogen atoms attached to it. Chemists represent this structure by the diagram shown in Fig. 15.2. Figure 15.2: The structure of the propane molecule. Chemists have found that it is not too difficult to discern the structure of simple compounds like propane. However, it is more difficult for more complex compounds, such as 2-methyl-hexan-3-one, a ketone with chemical formula C7H140. One method that chemists have used to infer the structure of a compound is to bombard it with high-energy electrons in a mass spectrometer. The electron beam of a mass spectrometer breaks the compound into fragments, and the resulting fragments are sorted according to their masses by a magnetic field within the spectrometer. A sample mass spectrogram is shown in Fig. 15.3. The fragments produced by the mass spectrometer tend to be composed of robust substructures of the compound, and the masses of these reveal hints about the main structure. An experienced chemist uses “accumulated knowledge” (to use Berliner’s phrase) about how compounds tend to break up in the mass spectrometer to make good guesses about a compound’s structure. Feigenbaum and Lederberg, together with their colleague Bruce Buchanan (1940– ), who had joined Stanford in 1966 after obtaining a Ph.D. in Philosophy at the University of Michigan, set about attempting to construct computer programs that could use mass spectrogram data, together with the chemical formula of a compound, to “elucidate” (as they put it) the structure of the compound. Lederberg had already developed a computer procedure called Dendral (an acronym for Dendritic Algorithm) that could generate all topologically possible acyclic structures given the chemical formula and other basic chemical information about how atoms attach to other atoms. (An acyclic structure is one that does not contain any rings. You might recall, for example, that 256 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 257. 15.0 Figure 15.3: A mass spectrogram. (Illustration used with permission of Edward Feigenbaum.) benzene contains six carbon atoms arranged in a hexagon, which chemists call a ring. Each of the carbon atoms has a hydrogen atom attached to it.) Lederberg’s algorithm proceeded incrementally by generating partial structures from the main formula, then generating more articulated partial structures from these and so on in a treelike fashion. The tips or leaves of the tree would contain the final, fully articulated topologically possible structures. Finding the actual structure of a compound (or at least the most plausible actual structures) can be likened to a search down the tree to the appropriate tip or tips. Feigenbaum and colleagues proposed using the knowledge that skilled chemists used when interpreting mass-spectral data. The chemists knew that certain features of the spectrograms implied that the molecule under study would contain certain substructures and would not contain other ones. This knowledge could be used to limit the possible structures generated by Lederberg’s Dendral algorithm. Knowledge of this sort was represented as “rules.” Here is one example of a Dendral rule: Rule 74: IF The spectrum for the molecule has two peaks at masses X1 and X2 such that: X1 + X2 = M + 28 and X1 - 28 is a high peak and Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 257
- 258. 15 The Dendral Project X2 - 28 is a high peak and at least one of X1 or X2 is high THEN The molecule contains a ketone group The first program to employ this kind of knowledge was called HEURISTIC DENDRAL. (The adjective “heuristic” was used because knowledge from the chemists was used to control search down the Dendral tree.) It used as input the chemical formula and mass-spectrometer data (and sometimes nuclear-magnetic-resonance data) and produced as output an ordered set of chemical structure descriptions hypothesized to explain the data. Early work with HEURISTIC DENDRAL was limited to elucidating the structure of acyclic compounds because these were the only ones that Lederberg’s algorithm could handle. These included saturated acyclic ethers, alcohols, thioethers, thiols, and amines. Here is one example of the power of their early program: There are 14,715,813 possible structures of N,N-dimethyl-1-octadecyl amine. Using the mass spectrum of that compound, HEURISTIC DENDRAL reduced the number to 1,284,792. Using the mass spectrum and nuclear-magnetic-resonance data, just one structure survived.3 The name “DENDRAL” came to describe a whole collection of programs for structure elucidation developed during the Dendral project, which continued to the end of the 1970s. Many of these programs are used by chemists today. Computer scientists and chemists working on the project were able to extend Lederberg’s algorithm to handle cyclic compounds. After Lederberg persuaded Stanford chemist Carl Djerassi to join the project, performance was expanded greatly in both breadth and depth.4 An important innovation made during the Dendral project was a simulation of how a chemical structure would break up in a mass spectrometer. After HEURISTIC DENDRAL produced some candidate structures for a particular compound, these structures were subjected to analysis in the simulated mass spectrometer. The outputs were then compared with the actual mass spectrometer output. That structure whose simulated spectrogram was closest to the actual spectrogram was likely to be the actual structure of the compound. This process of “analysis by synthesis” came to be widely used in artificial intelligence, especially in computer vision. From his experience during the DENDRAL years, Feigenbaum went on to champion the importance of specific knowledge about the problem domain in AI applications (as opposed to the use of general inference methods). He proposed what he called the “knowledge-is-power” hypothesis, which he later called the “knowledge principle.”5 Here is how he later described it:6 We must hypothesize from our experience to date that the problem solving power exhibited in an intelligent agent’s performance is primarily a consequence of the specialist’s knowledge employed by the agent, and only very secondarily related to the generality and 258 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 259. 15.0 NOTES power of the inference method employed. Our agents must be knowledge-rich, even if they are methods-poor. Embedding the knowledge of experts in AI programs led to the development of many “expert systems,” as we shall see later. It also led to increased concentration on specific and highly constrained problems and away from focusing on the general mechanisms of intelligence, whatever they might be. Notes 1. The quotation taken from “Comments by Edward A. Feigenbaum” in Edward H. Shortliffe and Thomas C. Rindfleisch, “Presentation of the Morris F. Collen Award to Joshua Lederberg,” Journal of the American Medical Informatics Association, Vol. 7, No. 3, pp. 326–332, May–June 2000. Available online at http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=61437. [255] 2. For an interesting account of the history of their collaboration, see “How DENDRAL Was Conceived and Born,” by Joshua Lederberg, a paper presented at the Association for Computing Machinery (ACM) Symposium on the History of Medical Informatics at the National Library of Medicine on November 5, 1987. Later published in Bruce I. Blum and Karen Duncan (eds.), A History of Medical Informatics, pp. 14–44, New York: Association for Computing Machinery Press, 1990. Typescript available online at http://profiles.nlm.nih.gov/BB/A/L/Y/P/ /bbalyp.pdf. [255] 3. Robert K. Lindsay, Bruce G. Buchanan, Edward A. Feigenbaum, and Joshua Lederberg, Applications of Artificial Intelligence for Organic Chemistry: The Dendral Project, p. 70, New York: McGraw-Hill Book Co., 1980. [258] 4. For a thorough account of achievements of the Dendral project, see ibid. [258] 5. The hypothesis seems to have been implicit in Edward A. Feigenbaum, “Artificial Intelligence: Themes in the Second Decade,” Supplement to Proceedings of the IFIP 68 International Congress, Edinburgh, August 1968. Published in A. J. H. Morrell (ed.), Information Processing 68, Vol. II, pp. 1008–1022, Amsterdam: North-Holland, 1969. [258] 6. Edward A. Feigenbaum, “The Art of Artificial Intelligence: Themes and Case Studies of Knowledge Engineering,” Proceedings of the Fifth International Joint Conference on Artificial Intelligence, pp. 1014–1029, 1977. See also Edward A. Feigenbaum, “The Art of Artificial Intelligence: I. Themes and Case Studies of Knowledge Engineering,” Stanford Heuristic Programming Project Memo HPP-77-25, August 1977, which is available online at http://infolab.stanford.edu/pub/cstr/reports/cs/tr/77/621/CS-TR-77-621.pdf. [258] Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 259
- 260. 15 NOTES 260 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 261. 16.0 Chapter 16 Conferences, Books, and Funding Accompanying the technical progress in artificial intelligence during this period, new conferences and workshops were begun, textbooks were written, and financial support for basic research grew and then waned a bit. The first large conference devoted exclusively to artificial intelligence was held in Washington, DC, in May 1969. Organized by Donald E. Walker (1928–1993) of the MITRE Corporation and Alistair Holden (1930–1999) of the University of Washington, it was called the International Joint Conference on Artificial Intelligence (IJCAI). It was sponsored by sixteen different technical societies (along with some of their subgroups) from the United States, Europe, and Japan. About 600 people attended the conference, and sixty-three papers were presented by authors from nine different countries. The papers were collected in a proceedings volume, which was made available at the conference to all of the attendees. Because of the success of this first conference, it was decided to hold a second one in London in 1971. During the early years, organization of the conferences was rather informal, decisions about future conferences being made by a core group of some of the leaders of the field who happened to show up at organizing meetings. At the 1971 meeting in London, I left the room for a moment while people were discussing where and when to hold the next conference. When I returned, I was informed that I had been selected to be the “czar” of the next meeting – to be held at Stanford University in 1973. Later, a more formal arrangement was instituted for managing the “International Joint Conferences on Artificial Intelligence,” with a President, a Board of Trustees, and a Secretariat.1 Since the first meeting, IJCAI conferences are held biennially (on odd-numbered years) with the venue alternating (loosely) between North America and the rest of the world. As at Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 261
- 262. 16 Conferences, Books, and Funding the first conference, proceedings are distributed at the conferences. (Some of these can be obtained from various booksellers, and they are available online at the Digital Library of India Web site, http://202.41.92.139/.) One of the oldest “special interest groups” of the Association for Computing Machinery (ACM) is SIGART (the Special Interest Group for ARTificial intelligence). It began publishing a Newsletter in 1966, which (as the SIGART Web site says) “continued in various incarnations (the SIGART Bulletin, Intelligence Magazine) until 2001.” Today, SIGART supports various conferences and workshops, and it organizes meetings in which AI doctoral students can present their nearly finished work to their peers and to senior researchers for comments and critiques. As the field began to develop its techniques and methods, graduate courses in artificial intelligence were offered at some universities. Accordingly, some of us who were teaching these courses thought it would be worthwhile to write or edit books about AI. In 1963, Edward Feigenbaum and Julian Feldman, then assistant professors at UC Berkeley, published a collection of early AI and cognitive science papers that had previously appeared in many different places. The volume was called Computers and Thought and was required reading for early students of AI (including me).2 As Feigenbaum wrote in the 1995 edition, “Some of the papers are as important today for their fundamental ideas as they were in the late 1950s and early 1960s when they were written. Others are interesting as early milestones of fields that have expanded and changed dramatically.” In 1965, I published a book about neural-network and statistical methods in pattern recognition.3 That book was followed in 1971 by a book about AI search strategies.4 Around the same time, other texts were published by James Slagle5 and by Bertram Raphael,6 both former Ph.D. students of Marvin Minsky at MIT. In 1969 Marvin Minsky and Seymour Papert published an influential book in which they proved, among other things, that some versions of Rosenblatt’s perceptrons had important limitations.7 Some have claimed that the Minsky–Papert book was the cause of a fading interest in neural-network research, but I doubt this. First, Rosenblatt himself began concentrating on other topics well before 1969,8 and the success of heuristic programming methods caused a shift of attention (including my own) away from neural networks during the mid-1960s. In 1965, Donald Michie at the University of Edinburgh organized the first of several invitation-only “Machine Intelligence” workshops. This first one was held in Edinburgh and was attended by American and European researchers. Attendees gave papers at the workshop, and these were all published in a book edited by N. L. Collins and Donald Michie in 1967. A second workshop was held in September 1966, also at the University of Edinburgh. Subsequent workshops were held annually in Edinburgh through 1971. Thereafter, the 262 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 263. 16.0 workshops were held every few years at various venues. Each workshop resulted in a book with the title Machine Intelligence N, where N denotes the workshop and volume number.9 The last few volumes have been published online by the Electronic Transactions on Artificial Intelligence.10 These books contain some of the most cited and important papers in the early history of the field. These years saw the United States engaged in war in Vietnam, and Congress wanted to make sure that research supported by the U.S. Defense Department was relevant to military needs. Responding to these pressures, on November 19, 1969, Congress passed the “Mansfield Amendment” to the Defense Procurement Authorization Act of 1970 (Public Law 91-121), which required that the Defense Department restrict its support of basic research to projects “with a direct and apparent relationship to a specific military function or operation.” On March 23, 1972, the Advanced Research Projects Agency was renamed the Defense Research Advanced Projects Agency (DARPA) to reflect its emphasis on projects that contributed to enhanced military capabilities. (The name reverted to the Advanced Research Projects Agency in 1993 and then back to the Defense Advanced Research Projects Agency in 1996.)11 On the other side of the Atlantic, British AI researchers experienced their own funding crisis. One of the U.K.’s main funding bodies for university research, the Science Research Council, asked Professor James Lighthill, a famous hydrodynamicist at Cambridge University, to undertake an evaluative study of artificial intelligence research. Lighthill’s report, titled “Artificial Intelligence: A General Survey,” somewhat idiosyncratically divided AI research into three categories, namely, advanced automation, computer-based studies of the central nervous system, and the bridges in between. He called these categories A, C, and B, respectively. Although he came out in favor of continued work in categories A and C, he was quite critical of most AI basic research, including robotics and language processing, which he lumped into category B. He wrote that “In no part of the field have the discoveries made so far produced the major impact that was then [around 1960] promised.” He concluded that AI’s existing search techniques (which worked on toy problems) would not scale to real problems because they would be stymied by the combinatorial explosion.12 Lighthill’s report resulted in a substantial curtailment of AI research in the United Kingdom. In particular, one of its casualties was work on FREDDY the robot and other AI work under Donald Michie at Edinburgh. Here is one of Michie’s later comments about the effects of the report:13 Work of excellence by talented young people was stigmatised as bad science and the experiment killed in mid-trajectory. This destruction of a co-operative human mechanism and of the careful craft of many hands is elsewhere described as a mishap. But to Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 263
- 264. 16 NOTES speak plainly, it was an outrage. In some later time, when the values and methods of science have further expanded, and those of adversary politics have contracted, it will be seen as such. DARPA’s shift to shorter term applied research, together with the Lighthill report and criticisms from various onlookers, posed difficulties for basic AI research during the next few years. Nevertheless, counter to Lighthill’s assessment, many AI techniques did begin to find application to real problems, launching a period of expansion in AI applications work, as we’ll see in the next few chapters. Notes 1. See http://www.ijcai.org/IJCAItrustees.php. [261] 2. Edward A. Feigenbaum and Julian Feldman, Computers and Thought, New York: McGraw-Hill Book Co., 1963. (The McGraw-Hill volume is now out of print; it is now available through AAAI Press/MIT Press, 1995 edition.) [262] 3. Nils J. Nilsson, Learning Machines: Foundations of Trainable Pattern-Classifying Systems, New York: McGraw-Hill Book Co., 1965; republished as The Mathematical Foundations of Learning Machines, San Francisco: Morgan Kaufmann Publishers, 1990. [262] 4. Nils J. Nilsson, Problem-Solving Methods in Artificial Intelligence, New York: McGraw-Hill Book Co., 1971. [262] 5. James R. Slagle, Artificial Intelligence: The Heuristic Programming Approach, New York: McGraw-Hill Book Co., 1971. [262] 6. Bertram Raphael, The Thinking Computer: Mind Inside Matter, New York: W. H. Freeman, 1976. [262] 7. Marvin Minsky and Seymour Papert, Perceptrons: An Introduction to Computational Geometry, Cambridge, MA: MIT Press, 1969. [262] 8. See Frank Rosenblatt, J. T. Farrow, and S. Rhine, “The Transfer of Learned Behavior from Trained to Untrained Rats by Means of Brain Extracts. I,” Proceedings of the National Academy of Sciences, Vol. 55, No. 3, pp. 548–555, March 1966. [262] 9. The series maintains a Web page at http://www.cs.york.ac.uk/mlg/MI/mi.html. [263] 10. See http://www.etai.info/mi/. [263] 11. See http://www.darpa.mil/body/arpa darpa.html. [263] 12. The text of the report, along with commentary and criticism by leading British AI researchers, was published in 1972 in James Lighthill et al. (eds.), Artificial Intelligence: A Paper Symposium, London: Science Research Council of Great Britain, 1972. [263] 13. Donald Michie, Machine Intelligence and Related Topics: An Information Scientist’s Weekend Book, p. 220, New York: Gordon and Breach Science Publishers, 1982. [263] 264 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 265. 16.0 Part IV Applications and Specializations: 1970s to Early 1980s Until about the early 1970s, most AI research dealt with what Seymour Papert called “toy” problems – programs that solved puzzles or games – or the researchers pursued projects that were staged in highly controlled laboratory settings. (Of course, there were some notable exceptions – machine translation, DENDRAL, and LUNAR, for example.) However, soon after, AI efforts began a definite shift toward applications work, confronting problems of real-world importance. Inevitably, successful applications work encouraged specialization into subdisciplines such as natural language processing, expert systems, and computer vision. One reason for the increasing interest in applications was that the power of AI methods had increased to the point where realistic applications seemed within reach. But perhaps more importantly, the sponsors of AI research in the U.S. Department of Defense (DoD) had to deal with the constraints imposed on them by the 1969 “Mansfield Amendment,” which required that basic research be relevant to military needs. As one example of the increased emphasis on applications, the Information Sciences Institute (USC-ISI) was formed in 1972 specifically to pursue them. Located in Marina Del Rey, California, it is affiliated with the University of Southern California and received much of its initial support from DARPA. Other large corporations also began to explore AI’s commercial potential. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 265
- 266. 16 Of course, theoretical and basic research continued also, and several new university groups joined the existing ones. A short list of the new ones would include those at the Universities of Toronto, Rochester, Texas, Maryland, British Columbia, California, and Washington. Other groups started as well in Europe and Asia. (In 1981, I was invited to give lectures on AI in China, which was newly recovering from its “Cultural Revolution” and beginning its program of “Opening and Reform.”) But even at the universities, much of their basic research was motivated by specific applications. In this part of the book, I’ll describe some of the AI applications work undertaken during the 1970s to the early 1980s. 266 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 267. 17.1 Chapter 17 Speech Recognition and Understanding Systems 17.1 Speech Processing The NLP systems I have already described required that their English input be in text format. Yet, there are several instances in which speaking to a computer would be preferable to typing at one. People can generally speak faster than they can type (about three words per second versus about one word per second), and they can speak while they are moving about. Also, speaking does not tie up hands or eyes. In discussing the problem of computer processing of speech, it is important to make some distinctions. One involves the difference between recognizing an isolated spoken word versus processing a continuous stream of speech. Most AI research has concentrated on the second and harder of these problems. Another distinction is between speech recognition and speech understanding. By speech recognition is meant the process of converting an acoustic stream of speech input, as gathered by a microphone and associated electronic equipment, into a text representation of its component words. This process is difficult because many acoustic streams sound similar but are composed of quite different words. (Consider, for example, the spoken versions of “There are many ways to recognize speech,” and “There are many ways to wreck a nice beach.”) Speech understanding, in contrast, requires that what is spoken be understood. An utterance can be said to be understood if it elicits an appropriate action or response, and this might even be possible without recognizing all of its words. Understanding speech is more difficult than understanding text because Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 267
- 268. 17 Speech Recognition and Understanding Systems there is the additional problem of processing the speech waveform to extract the words being uttered. Speech, as it is captured by a microphone, is converted into an electronic signal or waveform, which can be displayed on an oscilloscope. In Fig. 17.1, I show a waveform generated by a person saying “This is a test.” This diagram shows the amplitude (voltage) of the speech signal plotted against time. The sections of the waveform corresponding to the words are demarcated by the boxes at the top of the diagram. The boxes at the bottom show acoustical elements of these words, which are called “phones.” Figure 17.1: A speech waveform. (Used with permission of Gunish Rai Chawla.) In general, phones are the sounds that correspond to vowels or consonants. English speech is thought to be composed of forty or so different phones. Special alphabets have been devised to represent phones. One is the International Phonetic Alphabet (IPA), which contains the phones of all known languages. IPA uses several special characters that do not have standard computer (ASCII) codes. Another, containing just the phones used in American English and using only standard characters, is ARPAbet, which was developed during speech-processing research sponsored by DARPA. The phones boxed in Fig. 17.1 use the ARPAbet notation. The table in Fig. 17.2 shows the ARPAbet phones and sample words containing them. Early speech recognition systems attempted first to segment the speech waveform into its constituent phones and then to assemble the phones into words. To do so, the speech signal was first digitized, and various parameters, such as the frequency or pitch, were extracted. The ways in which the values of these parameters change in time were used to segment the waveform into units containing phones. Using dictionaries that associate the values of waveform parameters with phones and phones with words, the waveform was finally converted into text. The process sounds simple but it is actually quite complex because, among other things, the beginnings and endings of spoken words and their component phones overlap in complex patterns, and people often pronounce the same words in different ways. For example, the word 268 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 269. 17.2 Speech Processing Figure 17.2: Consonants and vowels in the ARPAbet phonetic alphabet. “you” might be pronounced differently in “are you” [aa r y uw] and “did you” [d ih d jh uh]. Attempts to recognize speech began at Bell Laboratories as far back as the 1930s. In 1952, engineers at Bell Labs built a system for recognizing the numbers “zero” through “nine” uttered by a single speaker.1 Other work was done in the 1950s and 1960s at RCA Laboratories, at MIT, in Japan, in England, and in the Soviet Union.2 Work accelerated in the 1970s, some of which I’ll describe next. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 269
- 270. 17 Speech Recognition and Understanding Systems 17.2 The Speech Understanding Study Group Larry Roberts, who went to DARPA in late 1966 as “chief scientist” in the Information Processing Techniques Office (IPTO) and later became its director, became intrigued with the idea of building systems that could understand speech. Cordell Green, by then serving as a lieutenant in the U.S. Army, was assigned to IPTO under Roberts in early 1970 and was put in charge of funding and monitoring AI research projects. According to Green, Roberts told him “Do a feasibility study on a system that can recognize speech.”3 So, at the end of March 1970, Green organized a meeting at Carnegie Mellon University of several of the DARPA contractors and others interested in speech processing to discuss the feasibility of speech understanding by computer. Among those attending the meeting were researchers from SDC, Lincoln Laboratory, MIT, CMU, SRI, and BBN. It was decided at the meeting to form a “study group” to assess the state of the art and to make recommendations concerning the launching of a major DARPA-supported project in speech understanding. The group was to be chaired by Allen Newell of CMU.4 During the March meeting, Roberts was persuaded to talk about the kind of speech-understanding system that he had in mind. According to the study group’s rendition of his remarks, Roberts was thinking about a system that could accept continuous speech from many cooperative users, over a telephone, using a vocabulary of 10,000 words, with less than 10% semantic error, in a few times real time, and be demonstrable in 1973. The study group held its first meeting at BBN on May 26 and 27, 1970. At that meeting, the group considered some specific tasks that the understanding system would be able to engage in. Among these were answering questions about data management, answering questions about the operational status of a computer, and consulting about a computer operating system. A final meeting of the group was held at SDC in Santa Monica on July 26–28, 1970. The recommendation of the group (in brief) was to aim for a system that could accept continuous speech, from many cooperative speakers of the “general American dialect,” over a good quality microphone (not a telephone), using a selected vocabulary of 1,000 words (not 10,000 words), with a “highly artificial syntax,” involving tasks such as data management or computer status (but not consulting), with less than 10% error, in a few times real time, and be demonstrable in 1976 (not 1973) with a moderate chance of success. A final report of the group was drafted after the meeting, delivered to DARPA, and eventually published in 1973.5 Although there had been much prior research in speech processing by computer (nicely summarized in the study group’s report), not everyone was 270 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 271. 17.3 The DARPA Speech Understanding Research Program optimistic about success. One naysayer was John R. Pierce, a researcher at Bell Laboratories, where much speech-recognition work had already taken place. In 1969, Pierce wrote a letter6 to the Journal of the Acoustical Society of America in which he claimed that most people working on speech recognition were acting like “mad scientists and untrustworthy engineers. The typical recognizer gets it into his head that he can solve ‘the problem.’” In the same letter, though, he also wrote that . . . performance would continue to be very limited unless the recognizing device understands what is being said with something of the facility of a native speaker (that is, better than a foreigner who is fluent in the language). If this is so, should people continue work toward speech recognition? Perhaps this is for people in the field to decide. [My italics.] 17.3 The DARPA Speech Understanding Research Program In fact, people in the field did decide. In October 1971, Roberts established at DARPA a five-year Speech Understanding Research (SUR) program based largely on the study group’s report. Its budget was about $3 million per year. CMU, Lincoln Laboratory, BBN, SDC, and SRI were contracted to build systems. Complementary research would be performed at Haskins Laboratories, the Speech Communications Research Laboratory, the Sperry Univac Speech Communications Department, and the University of California at Berkeley. In 1976 some of these efforts resulted in systems that were demonstrated and tested against the program’s goals. CMU developed two of these, HARPY and HEARSAY-II. BBN produced HWIM (Hear What I Mean). SRI and SDC formed a partnership in which SDC developed the acoustic processing components and SRI developed the parsing and semantic components. However, the SDC effort ran into difficulties with computer access, so the combined SRI/SDC system was never formally tested. I’ll briefly summarize the BBN work and then describe the CMU work in more detail.7 17.3.1 Work at BBN SPEECHLIS was the first speech understanding system developed at BBN. It was designed to answer spoken questions about the moon rocks database (the one used in BBN’s earlier LUNAR system). It was rather slow and was not systematically tested.8 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 271
- 272. 17 Speech Recognition and Understanding Systems HWIM was designed to be a travel budget manager’s automated assistant and was able to respond to spoken questions such as “How much is left in the speech understanding budget?”9 In its final version, HWIM was tested on two versions, each of sixty-four different utterances by three male speakers. Thirty-one of these sentences had previously been used by the system as it was being designed, so there might have been some implicit (if unintentional) built-in extra capability for dealing with those sentences. The sentences ranged in length from three to thirteen words. HWIM was able to respond correctly to 41% of the sentences and “close” to correctly to 23% more of them. The system did not respond at all to 20% of the sentences. Although both SPEECHLIS and HWIM pioneered new and important methods in speech understanding, HWIM’s performance was generally regarded as not meeting the original DARPA objectives. (Their designers claimed that the test was not indicative of HWIM’s potential and that they could have done better with more time.) 17.3.2 Work at CMU In 1969 Raj Reddy left Stanford to become a faculty member at Carnegie Mellon University. One of the first speech systems he and colleagues worked on at CMU was called HEARSAY (later renamed HEARSAY-I).10 It used a number of independent computational processes to recognize spoken moves in chess from a given board position, such as “king bishop pawn moves to bishop four.” It was during the early stages of this work, that DARPA formed the Speech Understanding Study Group and initiated work in speech understanding. A public demonstration of HEARSAY-I recognizing connected speech was given in June 1972. Three different speech recognition and understanding systems were developed at CMU under the umbrella of the DARPA speech understanding research effort. These were DRAGON, HARPY, and HEARSAY-II, and they all contributed important AI ideas. Work on these systems was led by Allen Newell, Raj Reddy, James Baker, Bruce Lowerre, Lee Erman, Victor Lesser, and Rick Hayes-Roth.11 A. DRAGON During the early days of CMU’s speech understanding research, a Ph.D. student, James K. Baker, began work on a speech understanding system he called “DRAGON.”12 (According to Allen Newell, the name DRAGON was meant “to indicate that it was an entirely different kind of beast from the AI systems being considered in the rest of the speech effort.”13 ) Like HEARSAY-I, DRAGON was designed to understand sentences about chess moves. DRAGON introduced powerful new techniques for speech processing – elaborations of which are used in most modern speech recognition systems. It 272 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 273. 17.3 The DARPA Speech Understanding Research Program used statistical techniques to make guesses about the most probable strings of words that might have produced the observed speech signal. It was an early example of the importation of probabilistic representations and associated computational methods into AI. We’ll see a good deal more of these in later chapters. I’ll try to explain the main ideas without using much mathematics. Using the notation introduced in Section 2.3.2, suppose we let x stand for a string of words and y stand for the speech waveform that is produced when x is spoken. (Actually, we’ll let y be some information-preserving representation of the waveform in terms of its easily measurable properties such as the amounts of energy the waveform contains in various frequency bands. For simplicity, I’ll continue to call y a waveform, even though I mean its representation, which might be different for different speech understanding systems.) Because the same speaker may say the same words somewhat differently on different occasions, and different speakers certainly will say them differently, the word string x does not completely determine what the speech waveform y will be. That is, given any x, we can only say what the probabilities of the different y’s might be. As described in Chapter 2, these probabilities are written in functional form as p(y | x) (read as the “probability of y given x”). In principle, the actual values of p(y | x) for some particular x, say x = X, could be estimated, for example, by having a number of speakers utter the word string X many different times and tabulating how frequently different speech waveforms y occur. This process would have to be repeated for many different word strings. DRAGON avoided this tedious tabulation in a way to be explained shortly. For speech recognition, however, we want to know the probability of a word string x, given the speech signal y, so that we can select the most probable x. That is, we want p(x | y) rather than p(y | x). We could use Bayes’s rule as before, to produce the desired probability as follows: p(x | y) = p(y | x)p(x)/p(y). Upon observing a particular waveform, say y = Y , here is how we would use the quantities in this formula to decide what word string x was most probably uttered: 1. Look up all the values of p(Y | x) for all of the values of x we are considering. (We don’t have to do this for all possible strings of words, but only for those allowed by the vocabulary and syntax of the specialized area appropriate to the speech understanding task – chess moves in the case of DRAGON.) 2. Multiply each of these values by p(x). (The decision should be biased in favor of likely word strings.) Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 273
- 274. 17 Speech Recognition and Understanding Systems 3. Select that x, say X, for which the product is the largest. [We can ignore dividing by p(y) because its value does not affect which p(x | Y ) is largest.] Although this process would work in principle, it is quite impractical computationally. Instead, DRAGON and other modern speech-recognition systems exploit the hierarchical structure involved in what is presumed to be the way a speech waveform is generated. There are various levels in this hierarchy that could be identified. To oversimplify a bit, at the top of the hierarchy a given semantic idea is expressed by a string of words obeying the syntactic rules of the language. The string of words, in turn, gives rise to a string of phones – the phonetic units. Finally, the phone string is expressed by a speech waveform at the bottom of the hierarchy. At each level, we have a sequence of entities, say, x1, x2, . . . xn, producing a sequence of other entities, say, y1, y2, . . . , yn. We can diagram the process as shown in Fig. 17.3. Figure 17.3: Two hierarchical levels in speech generation. The DRAGON system made some simplifying assumptions. It assumed that each xi in the sequence of x’s is influenced only by its immediate precedent, xi−1, and not by any other of the xi. This assumption is called the Markov assumption. [Andrey Andreyevich Markov (1856–1922) was a Russian mathematician. He used (what was later called) a Markov model to analyze the statistics of a sequence of 20,000 Russian letters taken from Pushkin’s 274 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 275. 17.3 The DARPA Speech Understanding Research Program novel Eugene Onegin.14 Markov models are used extensively in physics and engineering. Google uses the Markov assumption, for example, in its computation of page rank.] Of course, we know that each word in a sequence depends on more than just the immediately preceding word. Even so, the Markov assumption makes computations simpler and still allows good performance. Further, it was assumed that each yi was influenced only by xi and xi−1. All of these “influences” are probabilistic. That is, given quantities like x3 and x4, for example, the value of y4 is not completely determined. One can only say what the probabilities of the values of y4 might be; these are given by the functional expression p(y4 | x3, x4). Probability values for the y’s are thus given by what is called a “probabilistic function of a Markov process.” To produce estimates of these probabilities, statistics can be gathered during a “learning process” (in which a speaker utters a training set of sentences). DRAGON combined these separate levels into a network consisting of a hierarchy of probabilistic functions of Markov processes. Entities representing segments of the speech waveform were at the bottom, entities representing phones were in the middle, and entities representing words were at the top. At each level, Bayes’s rule was used to compute probabilities of the x’s given the y’s. Because only the speech waveform at the bottom level was actually observed, the phones and words were said to be “hidden.” For this reason, the entire network employed hidden Markov models (HMMs). DRAGON was the first example of the use of HMMs in AI. They had been developed previously for other purposes.15 Using this network, recognition of an utterance was then achieved by finding the highest probability path through the network. Computing the probabilities for syntactically valid word sequences, given the sequence of segments of the observed speech waveform, is a problem that is similar to one I described earlier, namely, computing the confidences of strings of characters on FORTRAN coding sheets (see p. 101). Again, a method based on dynamic programming was used. As Baker wrote, “The optimum path is found by an algorithm which, in effect, explores all possible paths in parallel.”16 At the end of the process, the most probable syntactically legal string of words is identified. The mathematical operations for making these computations are too complex to explain here, but they can be performed efficiently enough to make speech recognition practical. Although the DRAGON system was not among those that were finally tested against DARPA’s speech understanding system objectives, Baker claimed that its initial results were “very promising” and that in “its first test with live speech input, the system correctly recognized every word in all nine sentences in the test.”17 DRAGON became the basis for a commercial product, “Dragon Naturally Speaking,” first developed and marketed by Dragon Systems, a company founded by Baker and his wife, Janet. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 275
- 276. 17 Speech Recognition and Understanding Systems B. HARPY HARPY was a second system produced at CMU under DARPA’s speech understanding research effort. Bruce T. Lowerre designed and implemented the system as part of his Ph.D. research.18 HARPY combined some of the ideas of HEARSAY-I and DRAGON. Like DRAGON, it searched paths through a network to recognize a spoken sentence, but it did not annotate the links between nodes in the network with transition probabilities like DRAGON did. Like HEARSAY-I, HARPY used heuristic search methods. Versions of HARPY were developed for understanding spoken sentences about several different task areas. The main one involved being able to answer questions about, and to retrieve documents from, a database containing summaries (called “abstracts”) of AI papers. Here are some examples: “Which abstracts refer to theory of computation?” “List those articles.” “Are any by Feigenbaum and Feldman?” “What has McCarthy written since nineteen seventy-four?” HARPY could handle a vocabulary of 1,011 words. Instead of using a grammar with the conventional syntactic categories such as Noun, Adjective, and so on, HARPY used what is called a “semantic grammar,” one that has expanded categories such as Topic, Author, Year, and Publisher that were semantically related to its subject area, namely, data about AI papers. HARPY’s grammar was limited to handle just the set of sentences about authors and papers that HARPY was supposed to be able to recognize. The network was constructed from what were called “knowledge sources” (KSs), which consisted of information needed for the recognition process.19 The first of these encoded syntactic knowledge about the grammar. A second knowledge source used by HARPY described how each word in HARPY’s vocabulary might be pronounced. And, because in spoken language word boundaries overlap in ways that depend on the words involved, successful recognition requires a third knowledge source dealing with such phenomena. A fourth knowledge source specified the phones involved in the pronunciation of words and transitions between words. HARPY combined all of this knowledge into a giant network of phones representing all the possible ways that syntactically legal sentences might be spoken. Each “phone node” in the network was paired with a representation of a segment of a speech waveform, called a “spectral template,” expected to be associated with that particular phone. These templates were obtained initially by having a speaker read about 700 sentences. They could be “tuned” for a new speaker by having the speaker read about 20 selected sentences during a “learning” session. A partial network of phones is shown in Fig. 17.4 to 276 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 277. 17.3 The DARPA Speech Understanding Research Program illustrate the general idea. HARPY’s actual network had 15,000 nodes. The network is for those parts of the sentences that begin with “Tell me. . . ” and “Give me. . . .” The symbols inside the nodes represent phones, using DRAGON’s notation for them. Arrows represent possible transitions from one phone to the next. Note that there are multiple paths, corresponding to different ways to pronounce the words. Figure 17.4: A partial network of the phones that might occur in a spoken sentence. To recognize the words in a spoken sentence, the observed speech waveform was first divided into variable-length segments that were guessed to correspond to the sequence of phones in the waveform. A spectral template was computed for each of these segments. The recognition process then proceeded as follows: The spectral template corresponding to the first spectral segment in the speech waveform was compared against all of the templates corresponding to the phones at the beginning of the network. In reference to Fig. 17.4, these would include comparisons against templates for –, T, G, and IH2 because they were among the nodes in the network that could be reached in one step from the start node, namely, [. (Of course, in using the complete network rather than just the partial example just illustrated, several more comparisons would be made against templates of additional phone nodes reachable in one step from the start node.) The best few matches were noted, and the paths to these nodes were designated to be the best one-step partial paths through the network. At the next stage, the spectral template of the Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 277
- 278. 17 Speech Recognition and Understanding Systems next waveform segment was compared against the templates of all of those phone nodes reachable by extending the best one-step paths one more step. Using the values of the comparisons computed so far, a set of best two-step partial paths was identified. This process continues until the end of the network was reached. At that time the very best path found so far could be associated with the words associated with the nodes along that path. This word sequence was then produced as HARPY’s recognition decision. HARPY’s method of searching for a best path through the network can be compared with the A∗ heuristic search process described earlier. Whereas A∗ kept the entire search “frontier” available for possible further searching, HARPY kept on its frontier only those nodes on the best few paths found so far. (The number of nodes kept on the frontier was a parameter that could be set as needed to control search.) HARPY’s designers called this technique “beam search” because the nodes visited by the search process were limited to a narrow beam through the network. Because nodes not in the beam were eliminated as the process went on, it is possible that the best complete path found by HARPY might not be the overall best one in the network. (One of the eliminated nodes might be on this overall best path.) Even so, the path found usually corresponded to a correct interpretation of the spoken sentence. At the end of the DARPA speech understanding project, HARPY was tested on 100 sentences spoken by three male and two female speakers. It was able to understand over 95% of these sentences correctly, thereby meeting DARPA’s goal of less that 10% error. On average, HARPY executed about 30 million computer instructions to deal with one second of speech. Using a 0.4- million instructions per second (0.4 MIPS) machine (a DEC PDP-KA10), it would take over a minute to process a second of speech; although this is quite a bit worse than real-time performance, it achieved DARPA’s goal of “a few times real time” (if we interpret “a few” somewhat accommodatingly). To put the real-time matter in perspective, today’s computers process billions of instructions per second. HARPY was the only system to meet DARPA’s goals. C. HEARSAY-II Finally, HEARSAY-II, a redesigned and improved version of HEARSAY-I, was perhaps the most ambitious of CMU’s speech projects.20 Like HARPY, HEARSAY-II was designed to answer questions about, and to retrieve documents from, a database containing abstracts of AI papers. (An earlier task considered was to retrieve wire-service news stories.) It too was limited to a vocabulary of 1,011 words and used a semantic grammar specialized to its subject area. The first steps in HEARSAY’s processing of an utterance involved segmenting the speech waveform and labeling the phones estimated to be present in each segment. HEARSAY then used a novel method of gradually building these components into syllables, the syllables into words, the words into word sequences, and finally word sequences into phrases. The phrases 278 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 279. 17.3 The DARPA Speech Understanding Research Program were then converted into appropriate routines for accessing the database of AI papers.21 The processing method used by HEARSAY involved a layered structure called a “Blackboard.” The labels of the phones estimated to be present, along with numbers related to their probabilities of occurrence, were “written” in one of the lower layers of the Blackboard. Specialized knowledge-source routines that “knew about” how syllables were constructed from phones “read” these labels and computed guesses about what syllables were in the utterance. These guesses, along with numbers measuring their confidences or likelihoods, were then written in the syllable layer of the Blackboard. Other knowledge-source routines that knew about how words were constructed from syllables read information already on the Blackboard and wrote guesses about words in the word layer of the Blackboard. And so on. HEARSAY-II had around 40 of these knowledge sources. The general idea is illustrated in Fig. 17.5. Figure 17.5: The Blackboard architecture. In principle, a knowledge source could read or write information on any layer of the Blackboard that was relevant to it. Moreover, it could do so in what is called an “asynchronous” manner – not dependent on when other Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 279
- 280. 17 Speech Recognition and Understanding Systems knowledge sources were doing their reading and writing. There were some knowledge sources that could write predictions about new words based on words already written in the word layer and on information in other layers. Knowledge sources could even write guesses about words in the word layer based on word sequences already written (with high confidence) in the sequence layer. This process of inferring what must be present in a lower layer (even though missed by initial processing) from what (from other evidence) is present in a higher layer is a theme that recurs often in later AI research. As far as I know, this extremely important AI innovation was first manifest in the HEARSAY-II system. According to Raj Reddy,22 one of the inventors of the Blackboard architecture (along with Victor Lesser, Lee Erman, and Frederick Hayes-Roth), Herbert Simon often used the word “blackboard” to describe the “working memory” component of the production system architecture he and Allen Newell were working with (see p. 577). A production system used IF–THEN rules (called productions), which were triggered by contents of the working memory and wrote new data in it. Reddy and team, recognizing the variety of different sources of knowledge relevant to speech processing, generalized the production system idea, extending the production rules into larger programs, renaming them “knowledge sources,” and elaborated working memory into the layered Blackboard structure. At the end of the DARPA speech understanding project, HEARSAY-II was tested on twenty-three spoken sentences, brand new to the system, having an average of seven words per sentence, and 81% of these were recognized word-for-word correctly, although 91% led to the same database query as would have a word-for-word correct sentence. HEARSAY’s designers claimed that this performance “comes close to meeting the ambitious goals. . . established for the DARPA program in 1971.” Although HEARSAY-II came close the results were not quite as good as those of HARPY. Although the Blackboard architecture is no longer used in modern speech recognition systems, it was adopted by several other AI programs. (We’ll see one of these later in the book.) According to Russell and Norvig, “Blackboard systems are the foundation of modern user interface architectures.”23 17.3.3 Summary and Impact of the SUR Program CMU’s HEARSAY-II and HARPY were demonstrated at CMU on September 8, 1976, and BBN’s HWIM was demonstrated at BBN on September 10. In a summary report of the projects, MIT’s Dennis Klatt wrote that “it is unclear whether there are large differences in ability among [these] three systems. However, only [HARPY] was able to meet the ARPA goals.”24 The developers of HEARSAY-II attributed HARPY’s superior performance to three factors: its more thorough search of potential solutions (permitted by 280 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 281. 17.4 Subsequent Work in Speech Recognition its precomputed network of all the sentences that might have been spoken), its more thorough built-in knowledge of transition phenomena between adjacent words, and its more thorough testing, tuning, and debugging.25 Some researchers and DARPA program managers, however, argued about the way in which the tests were carried out and claimed that none of the systems met the SUR program objectives. In any case, DARPA decided not to fund a proposed follow-on program. The program did show, however, that speech understanding was a reasonable technical goal and stimulated progress in speech processing technologies, notably in system organization, syntax and semantics, and acoustic processing. A National Research Council report concluded that “DARPA’s funding of research on understanding speech has been extremely important. . . . the results of this research have been incorporated into the products of established companies, such as IBM and BBN, as well as start-ups such as Nuance Communications (an SRI spinoff) and Dragon Systems. . . . The leading commercial speech-recognition program on the market today, the Dragon “NaturallySpeaking” software [now sold by Nuance], traces its roots directly back to the work done at CMU between 1971 and 1975 as part of SUR. . . .”26 17.4 Subsequent Work in Speech Recognition Speech recognition research was also being carried out in other laboratories besides those that were directly involved with DARPA’s SUR program. For example, Frederick Jelinek of the Speech Processing Group in IBM’s Computer Sciences Department at the Thomas J. Watson Research Center in Yorktown Heights, New York, is credited with being an early proponent of the use of statistical methods (including hidden Markov models) in speech recognition.27 The HMM approach was ultimately adopted by all the leading speech recognition companies. In 1984, DARPA began funding speech recognition work again as part of its “Strategic Computing” program (a program that will be described in a later chapter). Participants included CMU, SRI, BBN, MIT, IBM, and Dragon Systems. Among the systems developed at CMU over the next several years, for example, were SPHINX by Kai-Fu Lee and others and JANUS, a multilingual speech recognition and translation system, by Alex Waibel and others. (These and other systems are available as open-source software from the “Speech at CMU” Web page, http://www.speech.cs.cmu.edu/. The page also has links to many other speech recognition laboratories.) Based on their work on DRAGON at CMU, James and Janet Baker founded Dragon Systems in 1982. In 1997, Dragon introduced “Dragon NaturallySpeaking,” a speech recognition program for personal computers. It had a vocabulary of 23,000 words.28 IBM followed with ViaVoice, and other companies, including Microsoft, also have speech recognition software. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 281
- 282. 17 NOTES The transcription of spoken sentences to their textual equivalents is now largely a solved problem. For example, high-quality speech recognition is commonly employed today in many automated telephone response systems. However, understanding natural language speech (or text) to permit general dialogs with computer systems, for example, remains a long-term research problem. I’ll continue my discussion of work on that problem in a later chapter. Notes 1. K. H. Davis, R. Biddulph, and S. Balashek, “Automatic Recognition of Spoken Digits,” Journal of the Acoustical Society of America, Vol. 24, No. 6, pp. 627–642, 1952. [269] 2. For a history of early work see B. H. Juang and Lawrence R. Rabiner, “Automatic Speech Recognition – A Brief History of the Technology Development,” available online at http://www.ece.ucsb.edu/Faculty/Rabiner/ece259/Reprints/ 354 LALI-ASRHistory-final-10-8.pdf; or Sadaoki Furui, “50 Years of Progress in Speech and Speaker Recognition,” available online at http://www.furui.cs.titech.ac.jp/publication/2005/SPCOM05.pdf. [269] 3. C. Cordell Green, “AI During IPTO’s Middle Years,” in Thomas C. Bartee (ed.), Expert Systems and Artificial Intelligence: Applications and Management, p. 240, Indianapolis: Howard W. Sams & Co., 1988. [270] 4. Other members of the group were Jeffrey Barnett of the Systems Development Corporation, James Forgie of Lincoln Laboratory, C. Cordell Green, then a lieutenant in the U.S. Army stationed at DARPA, Dennis Klatt of MIT, J. C. R. Licklider, then at MIT, John Munson of SRI, Raj Reddy of CMU, and William Woods of BBN. [270] 5. The report was published as a special issue of the journal Artificial Intelligence: Allen Newell et al., Speech Understanding Systems: Final Report of a Study Group, New York: American Elsevier Publishing Co., Inc., 1973. A draft of the report is available online in the Newell collection at http: //diva.library.cmu.edu/webapp/newell/item.jsp?q=box00105/fld08162/bdl0001/doc0001/. [270] 6. J. R. Pierce, “Whither Speech Recognition?,” Journal of the Acoustical Society of America, Vol. 46, No. 4, pp. 1049–1051, Part 2, 1969. Also see a rebuttal by Arthur Samuel and Pierce’s response to Samuel and to other rebuttals in Journal of the Acoustical Society of America, Vol. 47, No. 6, Part 2, pp. 1616–1617, 1970. [271] 7. For a description of the SRI work, see Donald E. Walker (ed.), Understanding Spoken Language, New York: Elsevier North-Holland, Inc., 1978. [271] 8. For more details, see William A. Woods, “Motivation and Overview of BBN SPEECHLIS: An Experimental Prototype for Speech Understanding Research,” IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. ASSP-23, No. 1, pp. 2–9, February 1975. [271] 9. See J. Wolf and William A. Woods, “The HWIM Speech Understanding System,” Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP ’77, Vol. 2, pp. 784–787, May 1977; also (for full details) William A. Woods et al., Speech Understanding Systems – Final Report, BBN Report No. 3438, Vols. I–V, Bolt, Beranek, and Newman, Inc., Cambridge, MA, 1976. [272] 10. D. Raj Reddy, Lee D. Erman, and Richard B. Neely, “A Model and a System for Machine Recognition of Speech,” IEEE Transactions on Audio and Electroacoustics, Vol. 282 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 283. 17.4 NOTES AU-21, No. 3, pp. 229–238, June 1973; and D. Raj Reddy, Lee D. Erman, R. D. Fennell, and Richard. B. Neely, “The HEARSAY Speech Understanding System: An Example of the Recognition Processes, in Proceedings of the 3rd International Joint Conference on Artificial Intelligence, pp. 185–183, Stanford, CA, August 1973. [272] 11. For background on the speech processing work at CMU during this period, see Lee D. Erman, “Overview of the HEARSAY Speech Understanding Research,” SIGART Newsletter, No. 56, pp. 9–16, February 1976. [272] 12. James K. Baker, “Stochastic Modeling as a Means of Automatic Speech Recognition,” doctoral dissertation, Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, 1975, and James K. Baker, “The DRAGON System – An Overview,” IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. ASSP-23, No. 1, February 1975. [272] 13. Allen Newell, “Harpy, Production Systems and Human Cognition, in Ronald A. Cole (ed.), Perception and Production of Fluent Speech, Hillsdale, NJ: Lawrence Erlbaum Associates, 1980. Available online as Carnegie Mellon University Technical Report CMU-CS-78-140 at http: //diva.library.cmu.edu/webapp/newell/item.jsp?q=box00089/fld06145/bdl0001/doc0001/. [272] 14. For a translation see A. A. Markov, “An Example of Statistical Investigation of the Text Eugene Onegin Concerning the Connection of Samples in Chains,” Science in Context, Vol. 19, No. 4, pp. 591-600, 2006. [275] 15. See L. E. Baum and J. A. Eagon, “An Inequality with Applications to Statistical Estimation for Probabilistic Functions of a Markov Process and to a Model for Ecology,” Bulletin of the American Medical Society, Vol. 73, pp. 360–363, 1967. Baker credits Baum with introducing him to the theory of a probabilistic function of a Markov process. [275] 16. James K. Baker, “The DRAGON System – An Overview,” IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. ASSP-23, No. 1, p. 24, February 1975. [275] 17. Ibid, p. 29. [275] 18. Bruce T. Lowerre, “The HARPY Speech Recognition System,” doctoral dissertation, Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, April 1976. [276] 19. I am basing my description of HARPY on Bruce Lowerre and Raj Reddy, “The HARPY Speech Understanding System,” Trends in Speech Recognition, Prentice Hall. Reprinted in A. Waibel and K. Lee (eds.), Readings in Speech Recognition, pp. 576–586, San Mateo, CA: Morgan Kaufmann Publishers, Inc., 1990. [276] 20. Lee D. Erman, Frederick Hayes-Roth, Victor R. Lesser, and D. Raj Reddy, “The HEARSAY-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty,” Computing Surveys, Vol. 12, No. 2, June 1980. [278] 21. For a detailed summary of how HEARSAY processed an example sentence, see ibid. [279] 22. Telephone conversation, August 14, 2008. [280] 23. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, second edition, p. 580, Upper Saddle River, NJ: Prentice Hall, 2003. [280] 24. Dennis H. Klatt, “Review of the ARPA Speech Understanding Project,” Journal of the Acoustical Society of America, Vol. 62, No. 2, pp. 1345–1366, December 1977. [280] 25. Lee D. Erman, Frederick Hayes-Roth, Victor R. Lesser, and D. Raj Reddy, op. cit.. [281] 26. Funding a Revolution: Government Support for Computing Research, Chapter 9, Committee on Innovations in Computing and Communications: Lessons from History, Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 283
- 284. 17 NOTES Computer Science and Telecommunications Board, Commission on Physical Sciences, Mathematics, and Applications, National Research Council, Washington, DC: National Academy Press, 1999. Available online at http://books.nap.edu/openbook.php?record id=6323&page=15. [281] 27. See, for example, Frederick Jelinek, “Continuous Speech Recognition by Statistical Methods, Proceedings of the IEEE, Vol. 64, No. 4, pp. 532–556, April 1976. [281] 28. Dragon NaturallySpeaking is now available through Nuance. [281] 284 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 285. 18.1 Chapter 18 Consulting Systems 18.1 The SRI Computer-Based Consultant As my colleagues and I at SRI cast about for ways to continue our planning and vision research we had been doing under the “Shakey the Robot” project, while satisfying DARPA’s interest in militarily relevant applications, we hit upon the problem of equipment maintenance, repair, and training. We pointed out that any technology that could reduce expenditures for these items and lessen the need for utilizing scarce human experts would be extremely important to the military. Furthermore, we said, this need “cannot be satisfied merely by writing more and better manuals. A sophisticated computer system seems to us essential.” We proposed to begin work on this problem by developing an environment in which an apprentice repair person would be given advice about how to take apart and reassemble a small air compressor.1 The advice would be generated (as needed by the apprentice) by a planning and execution system that would give suggestions about what to do next. The apprentice could ask for advice, using a predefined small vocabulary, which the system would analyze with state-of-the art speech recognition equipment, and then the system would answer, using a speech generation module. The system would also employ a machine-vision system to help keep track of progress and to observe components pointed to by the apprentice. The proposed setup is shown in Fig. 18.1. Although the project did not last long enough to achieve all of its goals, an early version of a “Computer-Based Consultant” (CBC) system was demonstrated in which the apprentice (played by Georgia Sutherland, one of the project team members) was asked to reassemble an air compressor (a Sears Model 17209, 1/2 HP compressor) that had previously been partially taken apart. Requests and responses by both the system and the apprentice were Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 285
- 286. 18 Consulting Systems Figure 18.1: Proposed arrangement for helping an apprentice. (Used with per- mission of SRI International.) spoken. Here is a sample of one of the interactions with the system (with my commentary in parentheses):2 System: Please assemble air compressor. Apprentice: How? (The apprentice asks for advice, and the system, using Sacerdoti’s NOAH system I described previously, creates a hierarchical assembly plan.) S: Install pump. (The system mentions the first action in the high-level plan NOAH created.) A: OK. (The apprentice knows how to install the pump and indicates that she does not need further advice about how to do it. At this stage 286 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 287. 18.1 The SRI Computer-Based Consultant of the CBC project, the system assumed, without visual checking, that the apprentice had completed this first step.) S: Install pump brace. (The system mentions the second action in the high-level plan.) A: How? (The apprentice does not know how to achieve this step and asks for advice.) S: Connect pump brace to pump. (The system mentions the first of the more detailed steps in the high-level action for installing the pump brace.) A: OK. (The apprentice knows how to achieve this step.) S: Connect pump brace to belt-housing frame. (The system mentions the next of the more detailed steps in the high-level action for installing the pump brace.) This type of interaction continues until the task is completed. The system kept track of the current state of assembly by using the procedural net created by the NOAH system (see p. 230). If the apprentice were to have asked “Why?” at any step, the system would have used the procedural net to construct an answer. The answer might have been something like “you must do x because it is one of the actions involved in doing y,” or “you must do x because you must do it first in order to do the next action, z.” Also, the apprentice could have asked for help in locating one of the major parts of the air compressor, and the system would have used its laser pointer to show her. Part of the procedural net computed for this example is shown in Fig. 18.2. The CBC project also provided an opportunity for SRI’s NLP group to try out some ideas they were developing about generating and understanding the sentences used in conversations. In the CBC project, the apprentice and the person giving advice are participating in a dialog about a task, namely, the task of working on an air compressor. The structure of the task, as modeled by the procedural network generated by NOAH, provided important pragmatic information useful for sentence understanding. This information was exploited in a system called TDUS (an acronym for Task Dialog Understanding System), which could engage in more complex dialogs than the spoken one just illustrated as it guided an apprentice through an assembly task.3 TDUS integrated the NOAH planning system with a natural language understanding system (having syntactic, semantic, and pragmatic components) to allow text-based conversations with the apprentice. I’ll use an example taken from a paper about TDUS to illustrate the role that the task structure plays in sentence understanding.4 Consider the following sentences: Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 287
- 288. 18 Consulting Systems Figure 18.2: Part of a procedural net for assembling an air compressor. (Used with permission of SRI International.) Speaker 1: Why did John take the pump apart? Speaker 2: He did it to fix it. Interpreting the referents of the italicized words in the second sentence is aided by referring to the task context established by the first sentence. “He” refers to John, “did it” refers to the disassembly task, and the second “it” refers to the pump. TDUS makes extensive use of the shifting “context” and goals of the dialog. As the developers of TDUS wrote,5 As a dialog progresses, the participants continually shift their focus of attention and thus form an evolving context against which utterances are produced and interpreted. A speaker provides a hearer with clues of what to look at and how to look at it – what to focus upon, how to focus upon it, and how wide or narrow the focusing should be. We have developed a representation for discourse focusing, procedures for using it in identifying objects referred to by noun phrases, and procedures for detecting and representing shifts in focusing. 288 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 289. 18.1 The SRI Computer-Based Consultant (The words“utterance,” “speaker,” and “hearer” are not to be taken literally. TDUS processed text-based language, not spoken language. In NLP research, these words are often used in a generalized sense to refer to sentences, sentence generators, and sentence receivers, whatever the medium.) Focus was the main interest of Barbara J. Grosz (1948– ; Fig. 18.3), who continued work on that topic and its role in NLP as a professor at Harvard University. Besides the mechanisms for dealing with contexts, goals, and focus, TDUS contained a grammar, called DIAGRAM,6 for recognizing many of the syntactic structures of English, means for representing and reasoning about processes and goals, and a framework for describing how different types of knowledge interact as the dialog unfolds. Figure 18.3: Barbara J. Grosz. (Photograph courtesy of photographer Tony Rinaldo.) A demonstration of the CBC system, like the one I described a few paragraphs ago, was given at SRI on April 23, 1975, for J. C. R. Licklider [who Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 289
- 290. 18 Consulting Systems had returned to head IPTO in 1973]. Recollecting impressions of his visit, Licklider later said7 The second time I was in DARPA, there were very impressive AI systems dealing with maintenance of equipment. I remember SRI had a program that described how to take a pump apart and put it back together. That’s not a terribly complicated device, but it was pretty impressive to see a computer that obviously understood all the parts of the pump and how they worked together. Because of Licklider’s encouragement, we were optimistic about continuing the CBC project and made plans for a system that would diagnose and give advice about repairing a military jeep engine. Unfortunately, one of DARPA IPTO’s new program managers, Colonel David Russell, was not buying it. After visiting SRI a few days before Licklider’s April 23 visit, Russell sent an e-mail to Licklider saying8 I must admit to considerable concern over the SRI program, particularly in light of the management pressures on the AI program. Looking at the projected program plan that Nils has been working on, I see a 2.2M dollar program over the next three years with the aim of developing an experimental CBC for a jeep. . . . I can’t see how it can be defended as a near-term application. . . While it may be difficult, I would suggest that you give serious thought to terminating the CBC program when it completes the air-compressor phase and redirect SRI to more Defense oriented applications or pass their work to NSF. I appreciate that this is heresy, but that is how I saw the situation. I didn’t directly discuss these comments with Nils although I did ask what he would do if the program were terminated. I may have formed a negative view based on an incorrect understanding of the program, and I didn’t want to upset the SRI group without your views of the program. Later that year, Russell replaced Licklider as Director of DARPA IPTO and terminated the CBC project. (Work on TDUS, however, continued under NSF support.) DARPA support for the SRI group was subsequently “redirected” to natural language interfaces to databases (which I’ll describe later) and to “image understanding” to aid photo interpreters. Some of us chose instead to seek non-DARPA support to work on computer-based consulting systems. Ongoing work at Stanford University on so-called expert systems encouraged us in that direction. 290 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 291. 18.2 Expert Systems 18.2 Expert Systems 18.2.1 MYCIN Stanford’s HEURISTIC DENDRAL project demonstrated the power of endowing computers with expert knowledge about chemistry and spectroscopy. Feigenbaum, Lederberg, and Buchanan, the senior members of the project, believed that a similar approach might work on a medical problem. In the early 1970s Buchanan began talking with Stanley Cohen, Chief of Clinical Pharmacology at Stanford’s Medical School, about Cohen’s computerized drug interaction warning system called MEDIPHOR. Around the same time, Edward (Ted) Shortliffe (1947– ; Fig. 18.4), a Stanford Medical School student, took a Stanford course on AI and also became an assistant on Cohen’s project. Together, Shortliffe, Buchanan, and Cohen conceived the idea of building a computer program that would consult with physicians about bacterial infections and therapy. Shortliffe named the program MYCIN, a common suffix for antibacterial agents. Such a program would need to contain diagnostic and treatment knowledge of experts in infectious diseases. Figure 18.4: Bruce Buchanan (left) and Ted Shortliffe (right). (Photograph courtesy of Ed Feigenbaum.) The first question in developing MYCIN was how to represent expert knowledge. Shortliffe and Buchanan thought that something similar to the Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 291
- 292. 18 Consulting Systems “IF–THEN rules” used in DENDRAL would be appropriate. When diagnosing what disease might be causing certain symptoms, as well as in prescribing therapy, physicians appear to be using a kind of IF–THEN reasoning: IF the symptoms are such-and-such, THEN the cause is likely to be so-and-so. The knowledge behind this sort of reasoning is based on experience with cases as well as on scientific knowledge about diseases. It was believed that the IF–THEN knowledge needed by the program could be obtained by interviewing the appropriate medical experts who already thought in those terms. Interestingly, IF–THEN reasoning about medical matters has a long history. Summarizing part of a book by J. H. Breasted9 about surgical knowledge contained in an ancient Egyptian papyrus, Robert H. Wilkins wrote “The Edwin Smith Surgical Papyrus, dating from the seventeenth century B.C., is one of the oldest of all known medical papyri.”10 (The papyrus was bought in a Luxor antique shop by Edwin Smith in 1882.) Wilkins goes on to mention several rules from the papyrus, one of which is the following: Case Thirty Title: Instructions concerning a sprain in a vertebra of his neck. Examination: If thou examinest a man having a sprain in a vertebra of his neck, thou shouldst say to him: “look at thy two shoulders and thy breast.” When he does so, the seeing possible to him is painful. Diagnosis: Thou shouldst say concerning him: “One having a sprain in a vertebra of his neck. An ailment which I will treat.” Treatment: Thou shouldst bind it with fresh meat the first day. Now afterward thou shouldst treat [with] ywrw (and) honey every day until he recovers. Two other experts who joined in the development of the nascent diagnostic and treatment system were Thomas Merigan, Chief of the Infectious Disease Division at Stanford, and Stanton Axline, a physician in that division. In their summary11 of the history of the project, Buchanan and Shortliffe credit Axline with coming up with the name MYCIN for the program. The team submitted a successful grant application to the National Institutes of Health in October of 1973. Shortliffe decided to combine his medical studies with work toward a Computer Science Ph.D. based on MYCIN. Since the version of LISP he wanted to use (BBN-LISP, soon to become INTERLISP) was not available at Stanford, he used the SRI AI group’s PDP-10 computer. The IF–THEN rules elicited from the medical experts usually were hedged with uncertainty. Buchanan and Shortliffe mention that “Cohen and Axline used words such as ‘suggests’ or ‘lends credence to’ in describing the 292 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 293. 18.2 Expert Systems effect of a set of observations on the corresponding conclusion. It seemed clear that we needed to handle probabilistic statements in our rules. . . ” After wrestling with various ways to use probabilities to qualify MYCIN’s IF–THEN rules, Shortliffe finally decided on using the somewhat ad hoc notion of “certainty factors.”12 Here, for example (in both its internal LISP form and its English translation), is one of MYCIN’s rules: RULE036 PREMISE: ($AND (SAME CNTXT GRAM GRAMNEG) (SAME CNTXTM MORPH ROD) (SAME CNTXT AIR ANAEROBIC)) ACTION: (CONCLUDE CNTXT IDENTITY BACTEROIDES TALLY 0.6) IF: 1) The gram stain of the organism is gramneg, and 2) The morphology of the organism is rod, and 3) The aerobicity of the organism is anaerobic THEN: There is suggestive evidence (0.6) that the identity of the organism is bacteroides The 0.6 in this rule is meant to measure the expert’s “degree of belief” in or “certainty” about the conclusion. Shortliffe thought that a degree of belief was not the same as a probability assessment because, among other things, he noted that the experts who provided Rule 036 did not necessarily think that the probability of the organism not being bacteroides would be 0.4. The original MYCIN system had 200 such rules. By 1978, it had almost 500. MYCIN’s rules were usually evoked in a backward-reasoning fashion. For example, a rule of the form “IF x1 and x2, THEN y” would be used if the system’s overall goal was to conclude y. The use of this rule would lead to the use of rules whose “THEN” parts were either x1 or x2. At the end of a chain of rules, a physician user of the system (or a database) would be asked to supply information about the “IF” part. So, if MYCIN were trying to establish that the identity of an organism was bacteroides, RULE036 would be used and the physician (or database) would be asked if the gram stain of the organism is gramneg and so on.13 MYCIN was configured as a “consulting system.” That is, it interacted with a physician user who supplied information about a specific patient. The use of rules and rule-chaining allowed the system to provide “explanations” for its reasoning. For example, after a query to the user evoked by Rule 036, if the user asked “Why did you ask whether the morphology of the organism is rod,” the system would reply (in English) something like “because I am trying to determine whether the identity of the organism is bacteroides.” So, how did MYCIN do at its primary task of recommending therapy? Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 293
- 294. 18 Consulting Systems Shortliffe and colleagues conducted several evaluations in which physicians were asked to compare MYCIN’s recommendations with their own for several patients. Their major conclusion was that “Seventy percent of MYCIN’s therapies were rated as acceptable by a majority of the evaluators.” They also noted, by the way, that “75% is in fact better than the degree of agreement that could generally be achieved by Stanford faculty being assessed under the same criteria.”14 One of MYCIN’s innovations (as contrasted with DENDRAL, say) was that its reasoning process (using the rules) was quite separate from its medical knowledge (the rules themselves). Thus, it became common to divide the program into two parts, namely, the “inference engine” for applying rules and the “knowledge base” of rules. In principle, new rules could be added without having to change the inference engine. This division is shown in Fig. 18.5. This separation suggests that one could construct expert systems for other applications simply by replacing the medical knowledge with some other knowledge base without having to change the inference engine. William van Melle implemented a system he called EMYCIN (“E” for “empty”) for doing just that.15 A system designer along with experts in some field, X, could interact with EMYCIN to produce IF–THEN rules for field X. Using its built-in inference engine, EMYCIN could then use these rules to provide advice to a user of the system during a consultation. EMYCIN was used to build several different expert systems in fields as diverse as tax planning and mechanical structural analysis. Figure 18.5: The structure of a MYCIN-style expert system. Researchers soon discovered that a minor variation of the certainty factors used by MYCIN and EMYCIN was equivalent to using probabilities instead. This linkage to probability theory implied consequences that neither MYCIN nor EMYCIN could escape. In particular, their reasoning was consistent with probability theory only under some rather restrictive assumptions about how rules were used. As Russell and Norvig point out, if these assumptions aren’t 294 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 295. 18.2 Expert Systems met “certainty factors could yield disastrously incorrect degrees of belief through overcounting of evidence. As rule sets became larger, undesirable interactions between rules became more common, and practitioners found that the certainty factors of many other rules had to be ‘tweaked’ when more rules were added.”16 Modern methods use more sophisticated probabilistic techniques, as we shall see in a later chapter. Even so, the success of MYCIN and the various EMYCIN programs led to the development of many more expert systems, some based on EMYCIN and some using their own specific approaches. As Allen Newell wrote in his introduction to a book by Buchanan and Shortliffe, “MYCIN is the original expert system that made it evident to all the rest of the world that a new niche had opened up. . . . MYCIN epitomized the new path that had been created. Thus, gathering together the full record of this system and the internal history of its development serves to record an important event in the history of AI.”17 18.2.2 PROSPECTOR Inspired by Shortliffe’s work with MYCIN, some of us at SRI began investigating nonmedical applications of expert systems. One area we considered was “integrated pest management” in which knowledge about crops and their insect pests could be used to mitigate the effects of insect predation with minimal use of chemical insecticides. Although proposals were written and some interest was shown by scientists in the U.S. Department of Agriculture and at the Environmental Protection Agency, the idea was abandoned when the proposals went unfunded. Peter Hart and Richard Duda eventually focused on systems for providing advice to explorationists about possible “hard-rock” mineral deposits.18 Hart had some early discussions with John Harbaugh, a petroleum engineering professor at Stanford, and with Alan Campbell, one of Harbaugh’s graduate students. (Alan Campbell was the son of the late Neal Campbell, a world-famous explorationist who had discovered what was possibly the largest lead–zinc deposit in the world. Alan spent much of his youth in mining camps.) Through Campbell, Hart and Duda met Charles Park, the former Dean of Stanford’s School of Earth Sciences and an authority on hard-rock mineral deposits. Park helped Hart and Duda codify knowledge about lead–zinc deposits in the form of IF–THEN rules. Further work with Marco Einaudi, a professor in Stanford’s Department of Economic Geology, led to additional rules and rule-organizing ideas. Ultimately the U.S. Geological Survey provided funding for the development of what became the PROSPECTOR expert system for consultation about mineral deposits.19 A large group of people participated in the design and writing of the PROSPECTOR program. Duda and Hart led the effort. I joined the project sometime after work had begun and after hearing from DARPA that the CBC project was not going to be continued. Other contributors were John Gaschnig Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 295
- 296. 18 Consulting Systems (1950–1982), Kurt Konolige, René Reboh, John Reiter, Tore Risch, and Georgia Sutherland. MYCIN was a dominant influence on the technology being developed – “primarily through its use of rules to represent judgmental knowledge, and its inclusion of formal mechanisms for handling uncertainty.”20 Other important influences came from another medical diagnosis system, INTERNIST-1, which I’ll describe shortly. These were its use of taxonomic information and its ability to handle volunteered (rather than only queried) information. PROSPECTOR used rules to make inferences and to guide the consultation process. Two examples of these rules are Rule 3: “Barite overlying sulfides suggests the possible presence of a massive sulfide deposit.” and Rule 22: “Rocks with crystal-shaped cavities suggest the presence of sulfides.” The rules were encoded as “partitioned semantic networks” – a format originated by Gary Hendrix (1948– ) in his University of Texas Ph.D. thesis for use in representing knowledge needed by natural language processing systems.21 Semantic networks were also used to represent the taxonomic knowledge used by PROSPECTOR. An example of such a network is shown in Fig. 18.6. The rules could be linked together in what was called an “inference network.” A simplified example for reasoning about a Kuroko-type massive sulfide deposit is shown in Fig. 18.7. Note how Rule 22 helps to establish one of the premises for Rule 3. Note also that the taxonomy is used to infer the presence of sulfides when galena, sphalerite, or chalcopyrite is known to be present. Inferences from rule premises to rule conclusions in the network depended on probabilities and Bayes’s rule – not on ad hoc numbers such as “certainty factors.” The geological experts were asked to quantify their uncertainty about a rule by giving the designers two numbers. One is the factor by which the odds favoring the conclusion would be increased if the premises were true. The other is the factor by which the odds favoring the conclusion would be decreased if the premises were false. Bayes’s rule was used in association with these numbers to derive the probability of the conclusion given the probabilities of the premises.22 PROSPECTOR’s inference methods, even though they were an improvement over those of MYCIN, gave probabilistically valid results only for certain kinds of inference-net structures. As Glenn Shafer and Judea Pearl explain, “Probabilities could not simply tag along as numbers attached to IF–THEN rules. The results of probability calculations would be sensible only if these calculations followed principles from probability theory.”23 Modern expert systems use the more general framework of Bayesian networks, which will be described later. 296 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 297. 18.2 Expert Systems Figure 18.6: A partial geologic taxonomy. (Used with permission of SRI Inter- national.) Figure 18.7: Simplified version of a PROSPECTOR inference network. (Used with permission of SRI International.) The usual format for a PROSPECTOR consultation involved a session with a geologist interested in evaluating a certain site. The geologist might volunteer some information, which would evoke some of PROSPECTOR’s rules. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 297
- 298. 18 Consulting Systems The system then calculated what additional information would be most effective in altering the probability of whatever it was the geologist wanted to find out. PROSPECTOR would then ask a question to elicit that information (and its probability). Throughout the process, the user could volunteer additional information at any time. Because PROSPECTOR could use volunteered information, a run of the program need not be part of a question-and-answer consultation session. Instead, a user could input a whole set of data about “findings in the field” to PROSPECTOR, which would then draw its conclusions. These findings could be from a database or, perhaps more usefully, from a map that indicated contours of regions in which various kinds of minerals were found to be present. (Kurt Konolige of SRI joined the PROSPECTOR team around this time and wrote a program that allowed PROSPECTOR to use map data as an input.) The most dramatic instance of PROSPECTOR’s use of map data occurred when it successfully identified the location of a porphyry molybdenum deposit at Mount Tolman in the state of Washington.24 Results of previous exploration of the Mount Tolman site were used to produce maps outlining important geological data relevant to potential molybdenum deposits. PROSPECTOR processed these maps using rules obtained primarily from Victor F. Hollister, an expert on porphyry molybdenum deposits, and Alan Campbell. The result of the processing was another map indicating the relative “favorability” of a mineral deposit. Computer displays of some of the input maps are shown in Fig. 18.8. I won’t explain the geological details of what these maps depict, but they represent the kind of data thought to be important by experts such as Campbell and Hollister. From data of this sort, PROSPECTOR produced favorability maps, one of which is shown in Fig. 18.9. The scale on the right of the map (when rendered in color) indicates favorability from +5 (highly favorable) through −5 (highly unfavorable). Based on previous extensive drilling in the largest of the favorable areas, a mining company had planned an open-pit mine there (outlined by the contour labeled “proposed pit”). One must be careful in evaluating this result. It is not the case that PROSPECTOR discovered an ore deposit in a site previously unexplored. As was pointed out in a letter to the editor of the journal Artificial Intelligence,25 A large mining company had already found a molybdenum ore body by drilling over 200 exploration holes in one region. . . and we knew that they intended to do further drilling for their own information. . . . [This further drilling] showed a remarkable congruence with PROSPECTOR’s favorability map, including both verification of PROSPECTOR’s prediction of a large, previously unknown region of 298 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 299. 18.2 Expert Systems Figure 18.8: Some of the Mount Tolman input maps. (Photographs courtesy of Richard Duda.) ore-grade mineralization, and verification of PROSPECTOR’s predictions for the barren areas. . . . Unfortunately, prolonged depressed economic conditions in the minerals industry have made this area unprofitable to mine. . . Thus, PROSPECTOR’s success to date has been scientific rather than economic. Readers interested in more details should see the Science article previously cited and a summarizing final report on the PROSPECTOR project.26 The computer code for PROSPECTOR was delivered to the U.S. Geological Survey where Richard B. McCammon developed a successor system he called PROSPECTOR II. Summarizing his system, McCammon wrote27 PROSPECTOR II, the successor to PROSPECTOR, was developed at Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 299
- 300. 18 Consulting Systems Figure 18.9: Favorability map. (Photograph courtesy of Richard Duda.) the US Geological Survey. Currently, the knowledge base contains 86 deposit models and information on more than 140 mineral deposits. Within minutes, the geologist can enter the observed data for an area, select the types of deposit models to be evaluated, receive advice on those models that best match the observed data, and, for a particular model, find out which of the data can be explained, which of the data are unexplained, and which critical attributes of the model are not observed in the data. 18.2.3 Other Expert Systems Several other expert systems followed the MYCIN and PROSPECTOR work. Some, like MYCIN, were for medical diagnosis and therapy.28 Of these, I’ll mention the INTERNIST-1 program by computer scientists Randolph A. Miller and Harry E. Pople and physician Jack D. Myers at the University of 300 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 301. 18.2 Expert Systems Pittsburgh and the CASNET (Causal-ASsociational NETwork) program by Casimir A. Kulikowski and Sholom M. Weiss of Rutgers University. The INTERNIST-1 series of diagnosis programs contained expertise about internal medicine.29 Part of this knowledge was represented in a kind of semantic network or taxonomy of disease states (called a nosology in medicine). In an article in the New England Journal of Medicine, Miller, Pople, and Myers state that the performance of INTERNIST-1 “on a series of 19 clinicopathological exercises (Case Records of the Massachusetts General Hospital) published in the Journal appeared qualitatively similar to that of the hospital clinicians but inferior to that of the case discussants.” However, they concluded that “the present form of the program is not [yet] sufficiently reliable for clinical applications.”30 Later, much of the diagnostic knowledge assembled in INTERNIST-1 was repackaged in QMR (Quick Medical Reference), a diagnostic decision support system for internists.31 (It has since been discontinued by its eventual purchaser First DataBank.) CASNET also used networks.32 In those, “inference rules” linked observations, patho-physiological states, diagnostic states, and treatment states. Their primary application was to the glaucomas, for which they had good physical models on which the inference rules could be based. At Carnegie Mellon University, John McDermott (1942– ) helped in the development of a rule-based system called XCON (for eXpert CONfigurer) to assist in the ordering and configuring of Digital Equipment Corporation’s VAX computer systems. XCON grew out of an earlier system by McDermott called R1.33 R1 and XCON were written in a special rule-processing language called OPS5, one of the OPS family of languages developed by Charles Forgy (1949– ) at CMU.34 (OPS is said to be an acronym for Official Production System.) The OPS languages used Forgy’s “Rete” algorithm for efficiently stringing IF–THEN rules together.35 XCON first went into use in 1980 in DEC’s plant in Salem, New Hampshire.36 The problem with how to deal with uncertain information was avoided in XCON because it almost never encountered a configuration issue that it did not have enough certain knowledge to handle. By 1989, according to a paper about XCON and related configuration systems at DEC,37 these systems had a total of about 17,500 rules. The paper went on to say that . . . overall the net return to Digital is estimated to be in excess of $40 million per year. The use of the configuration systems insures that complete, consistently configured systems are shipped to the customer. Incomplete orders do not get through the process. In addition, XCON generates configurations which optimize system performance, so customers consistently get the best view of our products. Before the configuration systems, we would often ship the same parts configured differently. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 301
- 302. 18 Consulting Systems In addition to XCON and its DEC siblings, several expert systems were built and put in use by companies and research laboratories during the 1980s. In 1983, General Electric developed the Diesel Electric Locomotive Troubleshooting Aid (DELTA), a prototype system to assist railroad personnel in the maintenance of General Electric’s diesel-electric locomotives. The developers stated that it “can diagnose multiple problems with the locomotive and can suggest repair procedures to maintenance personnel.” It had 530 rules “partially representing the knowledge of a Senior Field Service Engineer.”38 Another example is JETA (Jet Engine Troubleshooting Assistant), developed by engineers at the National Research Council in Canada. According to a paper about JETA, it “has been applied to troubleshoot the General Electric J85-CAN-15 jet engine that powers the CF-5 trainer fighters used by the Canadian Air Force.”39 Knowledge about jet engines and their possible faults and symptoms are encoded in frames. Rules are used solely for “specific control functions embedded in a frame and for asynchronous user input.” An expert system called CCH-ES for credit analysis was put in use at the Credit Clearing House (CCH) division of Dun & Bradstreet (D&B) in July 1989. It contained approximately 800 rules and could handle online transactions when CCH customers called in for service or when analysts wanted to review cases. Batch cases were run when there were updates in the relevant databases. According to a paper about the system, “Analyst agreement with CCH-ES continues to be at approximately 98.5 percent on an ongoing basis. . . . [It] has been a major success at D&B. It has provided CCH with an automated credit analyst expert system that can provide expert-level credit analysis decisions consistently and at a high-quality level. Customers have uniformly praised the system.”40 More expert systems are described in the book The Rise of the Expert Company.41 In an appendix to that book, Paul Harmon lists over 130 expert systems in use during the mid- to late 1980s, including • Grain Marketing Advisor for helping farmers choose marketing or storage strategies for their grain crops, • ACE for helping telephone operating companies reduce the incidence of phone cable failures, • IDEA for helping technicians diagnose trouble situations in the Infotron IS4000 Local Area Network, • Diag 8100 for helping with the diagnosis of problems and failures in IBM 8100 computers at the Travelers Corporation, • Intelligent Peripheral Troubleshooter for helping to troubleshoot Hewlett-Packard disk drives, 302 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 303. 18.2 Expert Systems • SNAP for helping shoppers at Infomart (a Dallas computer store) assess their personal computer needs, • Pile Selection for helping designers at the Kajima Construction Company select piling material to be used in the foundations of buildings, • ExperTAX for helping to evaluate the application of new U.S. tax laws for clients of Coopers and Lybrand, and • Dipmeter Advisor for helping in the analysis of geological formations encountered in oil-well drilling. 18.2.4 Expert Companies New companies and divisions of established companies were started to develop and field these applications. The first of these was Teknowledge, organized by a group of Stanford faculty and researchers to market expert systems and to consult about expert systems. Teknowledge used EMYCIN as its basic technology. Another was Syntelligence, founded by Peter Hart and Richard Duda (along with some of the PROSPECTOR researchers) to market expert systems for insurance underwriting and loan credit analysis. At Syntelligence, expert systems were written in the SYNTEL language, developed by René Reboh and Tore Risch and based on ideas from PROSPECTOR. After leaving CMU, Charles Forgy founded Production Systems Technologies in 1983 “to develop and market state of the art rule-based tools.”42 Among other companies formed during this period were Aion Corporation, Helix Expert System, Ltd., Exsys, Inc., Inference Corporation, and IntelliCorp.43 Because it was not too difficult for clients who wanted expert systems to develop their own versions (which were able to run on low-cost workstations and personal computers), many of the expert systems companies ceased to exist, were bought by larger companies, or had to reorient their businesses to provide additional or related services. After the flurry of excitement over expert systems died down a bit in the 1980s and 1990s, some developers concentrated on systems for acquiring and deploying “business rules.” According to an organization called the Business Rules Group, a business rule is “a statement that defines or constrains some aspect of the business. It’s intended to assert business structure, or to control or influence the behavior of the business.”44 For example, a business rule might state “when our widget inventory is below 200, notify widget production.” Business rules take the form of IF–THEN statements, just like expert-system rules. In business applications, expert-system inference engines metamorphosed into business rule engines (BREs). They are used either to answer questions about business practices or to take actions such as placing orders or sending alerts.45 Some of the people who had been involved in providing expert systems software switched to business-rule software. For example, in 2002, Charles Forgy founded RulesPower, Inc., whose business Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 303
- 304. 18 NOTES rules management systems (BRMSs) used later versions of the Rete algorithm. (In 2005, RulesPower sold some of its assets to Fair Isaac Corporation, an analytics and decision management technology company, which has since changed its name to FICO.) Notes 1. See Nils J. Nilsson et al., “Plan for a Computer-Based Consultant System,” SRI AI Center, Technical Note 94, May 1974. (Available online at http://www.ai.sri.com/pubs/files/1298.pdf.) [285] 2. From Peter E. Hart, “Progress on a Computer Based Consultant,” SRI AI Center Technical Note 99, p. 23, January 1975. (Available online at http://www.ai.sri.com/pubs/files/1389.pdf.) [286] 3. Ann E. Robinson, Douglas E. Appelt, Barbara J. Grosz, Gary G. Hendrix, and Jane J. Robinson, “Interpreting Natural-Language Utterances in Dialogs About Tasks,” SRI AI Center Technical Note 210, March 15, 1980. (Available online at http://www.ai.sri.com/pubs/files/709.pdf.) [287] 4. Ibid, p. 11. [287] 5. Ibid, p. 11. [288] 6. Jane J. Robinson, “DIAGRAM,” SRI AI Center Technical Note No. 205, 1980; available online as SRI AI Center Technical Note 205, February 1980, at http://www.ai.sri.com/pubs/files/712.pdf. [289] 7. J. C. R. Licklider, “The Early Years: Founding IPTO,” in Thomas C. Bartee (ed.), Expert Systems and Artificial Intelligence: Applications and Management, p. 223, Indianapolis, IN: Howard W. Sams & Co., 1988. [290] 8. A copy of this e-mail is in my files. [290] 9. J. H. Breasted, The Edwin Smith Surgical Papyrus, two volumes., Chicago: University of Chicago Press, 1980. [292] 10. See http://www.neurosurgery.org/cybermuseum/pre20th/epapyrus.html for a copy of the Wilkins article. [292] 11. Bruce G. Buchanan and Edward H. Shortliffe (eds.), Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project, Reading, MA: Addison-Wesley, 1984. The book is now out of print but is available online at http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/RuleBasedExpertSystems. Shortliffe’s dissertation has been reprinted as a book: Edward H. Shortliffe, Computer-Based Medical Consultations: MYCIN, New York: Elsevier, 1976. [292] 12. Others too attempted to use ideas not strictly based on probability theory. Among these were Arthur Dempster and Glenn Shafer (see Glenn Shafer, A Mathematical Theory of Evidence, Princeton, NJ: Princeton University Press, 1976) and Lotfi Zadeh, who developed “fuzzy logic” (see, as just one of many sources, Lotfi A. Zadeh, “A Fuzzy-Algorithmic Approach to the Definition of Complex or Imprecise Concepts,” International Journal of Man-Machine Studies, Vol. 8, pp. 249–291, 1976, available online at http://www-bisc.cs.berkeley.edu/ZadehFA-1976.pdf.) I’ll mention these alternatives later in the book. [293] 13. For a full description of how MYCIN’s rules were acquired and used see Bruce G. Buchanan and Edward H. Shortliffe, op. cit.. [293] 304 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 305. 18.2 NOTES 14. Bruce G. Buchanan and Edward H. Shortliffe, op. cit., Chapters 30 and 31. [294] 15. EMYCIN is described in Bruce G. Buchanan and Edward H. Shortliffe, op. cit., Chapter 15. EMYCIN was the subject of van Melle’s Ph.D. dissertation: William van Melle, “A Domain-Independent System That Aids in Constructing Knowledge-Based Consultation Programs,” Stanford University Computer Science Department; see also Stanford Report Nos. STAN-CS-80-820 and HPP-80-22, 1980. [294] 16. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, second edition, p. 525, Upper Saddle River, NJ: Prentice Hall, 2003. [295] 17. Bruce G. Buchanan and Edward H. Shortliffe, op. cit. [295] 18. Economic geologists distinguish mineral deposits from ore deposits. An ore is a mineral that can be profitably extracted. Hard-rock minerals include copper, lead, zinc, and so on, but not hydrocarbons. [295] 19. PROSPECTOR was first described in Peter E. Hart, “Progress on a Computer-Based Consultant,” Proceedings of the International Joint Conference on Artificial Intelligence, Vol. 2, pp. 831–841, 1975. [295] 20. Richard O. Duda et al., “Semantic Network Representations in Rule-Based Inference Systems,” in D. A. Waterman and Frederick Hayes-Roth (eds.), Pattern-Directed Inference Systems, Orlando, FL: Academic Press, Inc., 1978. Available online at http://www.ai.sri.com/pubs/files/751.pdf. [296] 21. Gary G. Hendrix, “Partitioned Networks for the Mathematical Modeling of Natural Language Semantics,” Ph.D. thesis, University of Texas Computer Science Department, 1975. For a short paper, see Gary G. Hendrix, “Expanding the Utility of Semantic Networks Through Partitioning,” Proceedings of the Fourth International Conference on Artificial Intelligence, pp. 115–121, 1975. This paper also appeared as SRI AI Center Technical Note 105 and is available online at http://www.ai.sri.com/pubs/files/1380.pdf. [296] 22. For a description of PROSPECTOR’s inference methods see Richard O. Duda, Peter E. Hart, and Nils J. Nilsson, “Subjective Bayesian Methods for Rule-Based Inference Systems,” in Proceedings of the AFIPS National Computer Conference, Vol. 45, pp, 1075–1082, 1976. Reprinted in G. Shafer and J. Pearl (eds.), Readings in Uncertain Reasoning, pp. 274–281, San Francisco: Morgan Kaufmann Publishers, 1990. A version appears as SRI AI Center Technical Note 124 and is available online at http://www.ai.sri.com/pubs/files/755.pdf. [296] 23. Glenn Shafer and Judea Pearl (eds.), Readings in Uncertain Reasoning, San Francisco: Morgan Kaufmann Publishers, 1990. The book is no longer in print, but some of the chapters are available online at http://www.glennshafer.com/books/rur.html. [296] 24. Alan N. Campbell, Victor F. Hollister, Richard O. Duda, and Peter E. Hart, “Recognition of a Hidden Mineral Deposit by an Artificial Intelligence Program,” Science, Vol. 217, No. 4563, pp. 927–929, September 3, 1982. [298] 25. Richard O. Duda, Peter E. Hart, and René Reboh, letter to the editor, Artificial Intelligence, Vol. 26, pp. 359–360, 1985. [298] 26. Richard O. Duda, “The PROSPECTOR System for Mineral Exploration,” Final Report prepared for the Office of Resource Analysis, U.S. Geological Survey, Reston, VA 22090, April 1980. [299] 27. Richard B. McCammon, “ PROSPECTOR II – An Expert System for Mineral Deposit Models,” International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts, Vol. 33, No. 6, pp. 267A–267A(1), September 1996. See also Richard B. McCammon, “PROSPECTOR II,” in H. J. Antonisse, J. W. Benoit, and B. G. Silverman (eds.), Proceedings of the Annual AI Systems in Government Conference, pp. 88–92, March 1989, Washington, DC. [299] 28. See Peter Szolovits (ed.), Artificial Intelligence in Medicine, Boulder, CO: Westview Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 305
- 306. 18 NOTES Press, 1982. Available online at http://groups.csail.mit.edu/medg/ftp/psz/AIM82/ch0.html. [300] 29. Harry E. Pople Jr., “Heuristic Methods for Imposing Structure on Ill-Structured Problems: The Structuring of Medical Diagnostics,” Chapter 5 in Peter Szolovits (ed.), Artificial Intelligence in Medicine, Boulder, CO: Westview Press, 1982. Available online at http://groups.csail.mit.edu/medg/ftp/psz/AIM82/ch5.html. [301] 30. Randolph A. Miller et al., “INTERNIST-1: An Experimental Computer-Based Diagnostic Consultant for General Internal Medicine,” New England Journal of Medicine, Vol. 307, pp. 468–76, August 19, 1982. [301] 31. Randolph A. Miller et al., “The INTERNIST-1/Quick Medical Reference Project – Status Report,” The Western Journal of Medicine, Vol. 145, No. 6, pp. 816–822, 1986. Available online at http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=1307155&blobtype=pdf. [301] 32. Casimir A. Kulikowski and Sholom M. Weiss, “Representation of Expert Knowledge for Consultation: The CASNET and EXPERT Projects,” Chapter 2 in P. Szolovits (ed.), Artificial Intelligence in Medicine, Boulder, CO: Westview Press, 1982. Available online at http://groups.csail.mit.edu/medg/ftp/psz/AIM82/ch2.html. [301] 33. John McDermott, “R1: A Rule-Based Configurer of Computer Systems,” Artificial lntelligence, Vol. 19, No. 1, pp. 39–88, 1980. [301] 34. Charles Forgy, “OPS5 User’s Manual,” Technical Report CMU-CS-81-135, Carnegie Mellon University, 1981. See also Lee Brownston et al., Programming Expert Systems in OPS5, Reading, MA: Addison-Wesley, 1985. [301] 35. Charles Forgy, “Rete: A Fast Algorithm for the Many Pattern/Many Object Pattern Match Problem,” Artificial Intelligence, Vol. 19, pp. 17–37, 1982. [301] 36. See http://en.wikipedia.org/wiki/Xcon. [301] 37. Virginia E. Barker and Dennis E. O’Connor, “Expert Systems for Configuration at Digital: XCON and Beyond,” Communications of the ACM, Vol. 32, No. 3, pp. 298–318, March 1989. [301] 38. Piero P. Bonissone and H. E. Johnson Jr., “ DELTA: An Expert System for Diesel Electric Locomotive Repair,” Proceedings of the Joint Services Workshop on Artificial Intelligence in Maintenance, Boulder, CO, October 4–6, 1983, AD-A145349, pp. 397–413, June 1984 (Defense Technical Information Center Accession Number ADA145349.) [302] 39. Phillippe L. Davidson et al., “Intelligent Troubleshooting of Complex Machinery,” Proceedings of the Third International Conference on Industrial Engineering Applications of Artificial Intelligence Expert Systems, pp. 16–22, Charleston, South Carolina, USA, July 16–18, 1990. See also M. Halasz et al., “ JETA: A Knowledge-Based Approach to Aircraft Gas Turbine Engine Maintenance,” Journal of Applied Intelligence, Vol. 2, pp. 25–46, 1992. [302] 40. Roger Jambor et al., “The Credit Clearing House Expert System,” IAAI-91 Proceedings, pp. 255–269, 1991. [302] 41. Edward Feigenbaum, Pamela McCorduck, and H. Penny Nii, The Rise of the Expert Company: How Visionary Companies Are Using Artificial Intelligence to Achieve Higher Productivity and Profits, New York: Times Books, 1988. [302] 42. http://www.pst.com/. [303] 43. Harmon’s appendix, just cited, lists several companies as does http://dmoz.org/Computers/Artificial Intelligence/Companies/. [303] 44. From http://www.businessrulesgroup.org/defnbrg.shtml. [303] 45. I thank Paul Harmon, now Executive Editor of Business Process Trends 306 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 307. 18.2 NOTES (www.bptrends.com), for enlightening me about business rules. [303] Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 307
- 308. 18 NOTES 308 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 309. 19.1 Chapter 19 Understanding Queries and Signals 19.1 The Setting Up until about the mid-1970s, DARPA managers were able to cushion the impact of the Mansfield Amendment (which required that Defense Department research be relevant to military needs) by describing computer research programs in a way that emphasized applications. Larry Roberts, the Director of DARPA’s IPTO during the late 1960s and early 1970s, wrote1 The Mansfield Amendment created a particular problem during my stay at DARPA. It forced us to generate considerable paperwork and to have to defend things on a different basis. It made us have more development work compared to the research work in order to get a mix such that we could defend it. I don’t think I had to drop a project in our group due to the Mansfield Amendment, however. We could always find a way to defend computer science. . . The formal submissions to Congress for AI were written so that the possible impact was emphasized, not the theoretical considerations. Cordell Green, working under Roberts at IPTO, wrote2 Generally speaking, anything that came along in the AI field that we thought looked good was supported. . . One of my jobs was to defend the AI budget but that wasn’t terribly difficult. . . . all sorts of computer science is relevant because it will have a high impact on any large Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 309
- 310. 19 Understanding Queries and Signals information-processing organization, and the Defense Department is certainly such an organization. . . . all of this research should be kept alive because it had potential military relevance. By the mid-1970s, however, the pressure to produce militarily useful systems became much more intense. DARPA, which had been generously supporting rather undirected basic AI research, started to focus instead on solving “pressing DoD problems.” Although the director of DARPA’s IPTO at the time, J. C. R. Licklider, was as sympathetic as ever to basic research in AI, DARPA’s top management had entirely different attitudes. Licklider was having difficulties explaining his AI program to DARPA’s “front office.” The DARPA Director during the early 1970s, Stephen Lukasik, was (according to Licklider3 ) neither for nor against AI. He was for good management and he got the idea that maybe some of the AI stuff wasn’t being very well managed. . . . [He] had a fixed idea that a proposal is not a proposal unless it’s got milestones. I think that he believed that the more milestones, the better the proposal. . . . I think he was not developing a distaste for AI but a conviction that this is such an important field that the researchers have got to learn to live in a bigger, more rigid, more structured bureaucracy.” Lukasik’s view about how projects should be managed had a direct effect on DARPA-supported basic research in AI. For example, a “Quarterly Management Report” that I submitted in February 1975 describing progress on the SRI computer-based consultant caused Licklider to ask how the report might be recast to emphasize progress along certain paths in a “PERT Chart.” “What I would like to have,” he wrote me in a letter dated March 3, 1975, “is the PERT Chart – so that I can mark the accomplishments in red and see where you stand with respect to the overall pattern. . . . Do you have such a chart? If so, please send me a copy. If not, how about making one? It would really help us greatly here at ARPA.”4 Of course, in basic research, although one can describe generally the problems one is trying to solve, one can’t describe (ahead of time) what the solutions are going to be. In fact, as exploratory research progresses, new problems become apparent, so one can’t even describe all the problems ahead of time. One can’t make the kind of detailed plan for basic research that one can make for applying already developed technology to specific applications. Unfortunately, the management of DARPA was shifting from people who understood how to initiate and manage basic research to people who knew how to manage technology applications. The shift toward shorter term, intensely managed research became more pronounced when George Heilmeier (Fig. 19.1) replaced Stephen Lukasik as DARPA Director in 1975. Heilmeier came from RCA, where he had headed 310 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 311. 19.1 The Setting the research group that invented the first liquid crystal display. Licklider later wrote that Heilmeier “wanted to understand AI in the way he understood liquid crystal displays. . . ”5 Figure 19.1: George Heilmeier. (Photograph courtesy of DARPA.) One of the tasks that Heilmeier gave IPTO was to produce a “roadmap” (that is, a detailed plan) for its AI research program (and its other computer science programs too). This roadmap should summarize past accomplishments, indicate areas where existing technology could be applied to military problems, and show milestones along the way. This “guidance” from DARPA management caused great difficulties for Licklider, some of which were explained in an e-mail he sent to some leaders of AI research in April of 1975. (I was among the recipients of his “Easter Message,” e-mailed on April 2, 1975.) Here are some excerpts: The purpose of this Easter note is to bring you up to date on a Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 311
- 312. 19 Understanding Queries and Signals development in ARPA that concerns me greatly – and will, I think, also concern you. . . . . . . the prevailing direction in ARPA is to do research within the specific contexts of military problems and not to do research that does not have a military ‘buyer’ ready to take it over as soon as the concept gets well formulated. . . . [there are] strong pressures from the new Director, George Heilmeier, that IPTO ‘redirect’ the university AI efforts to work on problems that have real DoD validity. . . . . . the situation is complicated by the fact that ARPA has been supporting basic research at a rather high level for more than ten years (has spent more than $50 million on it), and it is natural for a new director, or even an old one, to ask, “What have we gotten out of it in terms of improvements in national defense?” According to Licklider’s Easter note, some of the things that Heilmeier thought IPTO could do for the Defense Department were the following: • get computers to read Morse code in the presence of other code and noise, • get computers to identify/detect key words in a stream of speech, • solve DoD’s “software problem,” • make a real contribution to command and control, and • do a good thing in sonar. Even though one of the items on Heilmeier’s list involved speech processing, one of the casualties of his tenure as Director of DARPA was the SUR Program. None of the systems that had been developed under the program could respond in real time, nor could they deal with large enough vocabularies. Heilmeier believed (probably with good reason) that speech understanding was still a basic research activity. Thus, he thought, it should be supported, say, by the National Science Foundation (NSF), and he rejected proposals for DARPA to continue it. Unfortunately, most of the research areas that were on Licklider’s own list (which was also mentioned in his Easter note) were not explicitly on Heilmeier’s. (I can’t resist mentioning one of the items on Licklider’s list: “Develop a system that will guide not-sufficiently-trained maintenance men through the maintenance of complex equipment.”) One of Heilmeier’s items was sufficiently vague, however, to justify work both in NLP and in computer vision. That was “command and control,” an activity that involves getting and presenting relevant information to commanders so that they can control military forces effectively. 312 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 313. 19.2 Natural Language Access to Computer Systems DARPA program officers Floyd Hollister and Col. David Russell were able to persuade DARPA management that text-based, natural language access to large, distributed databases would be an important component of command and control systems. They argued that the technology for such access was sufficiently far along for it to be applied in what they called “command-and-control test-bed systems.” After all, Bill Woods and colleagues at BBN had already demonstrated LUNAR, a natural language “front end” to databases about moon rocks. Several other researchers had also begun work on the problem of how to communicate with computers using English or some other natural language. (For example, there were over forty papers on NLP presented at the Fifth IJCAI in 1977 at MIT, and the February 1977 issue of the ACM’s SIGART Newsletter published 52 summaries of ongoing research on “Natural Language Interfaces.”) In the next part of this chapter, I’ll describe some of the accomplishments during this period on communicating with computers using natural language. A second area of great importance in command and control was automating the analysis of aerial photos. Spotting targets of military interest in these photos, such as roads, bridges, and military equipment, typically required hours of effort by intelligence analysts. Because techniques being developed by researchers in computer vision might provide tools to help human analysts, DARPA had good reasons to continue funding computer vision research. In 1976, it began the “Image Understanding ”(IU) program to develop the technology required for automatic and semiautomatic interpretation and analysis of military photographs and related images. Although initially conceived as a five-year program, it continued (with broader objectives) for well over twenty years. I’ll summarize the image understanding work, along with other computer vision research, in a subsequent chapter. Doing something about sonar was one of the items on Heilmeier’s list. In fact, in his Easter note Licklider wrote “One of [Heilmeier’s] main silver-bullet areas is underwater sound and sonar, and IPTO is in the process of ‘buying in’ on the HASP project (Ed Feigenbaum’s AI approach).” I’ll describe HASP and how DARPA “bought in” to the project toward the end of the chapter. 19.2 Natural Language Access to Computer Systems 19.2.1 LIFER At SRI, Gary Hendrix (Fig. 19.2) had been developing a system called LIFER (an acronym for Language Interface Facility with Elliptical and Recursive Features), programmed in INTERLISP, for rapid development of natural language “front ends” to databases and other software. LIFER allowed a nontechnical user to specify a subset of a natural language (for example, Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 313
- 314. 19 Understanding Queries and Signals Figure 19.2: Gary Hendrix. (Photograph courtesy of Gary Hendrix.) English) for interacting with a database system or other software. A parser contained within LIFER could then translate sentences and requests in this language into appropriate interactions with the software. LIFER had mechanisms for handling elliptical (that is, incomplete) inputs, for correcting spelling errors, and for allowing novices to extend the language through the use of paraphrases. An interesting feature of LIFER was that the language it could handle was defined in terms of “patterns,” which used semantic concepts in the domain of application. One such pattern, for example, might be WHAT IS THE <ATTRIBUTE> OF <PERSON> where the words WHAT, IS, THE, and OF are actual words that might occur in an English query and <ATTRIBUTE> and <PERSON> are “wild cards” that could match any word in predefined sets. <ATTRIBUTE> might be defined to match words such as AGE, WEIGHT, HEIGHT, etc., and <PERSON> might match JOHN, SUSAN, TOM, etc. This pattern would then “recognize” a sentence such as WHAT IS THE HEIGHT OF SUSAN This method of defining a “grammar” is to be contrasted with the usual syntactic phrase-structure rules such as S <= NP VP. As I mentioned earlier, 314 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 315. 19.2 Natural Language Access to Computer Systems grammars based on concepts in the domain of application are called “semantic grammars.” LIFER used a simplified augmented transition network (like those I described in a previous chapter) to analyze an input sentence. Each pattern defined by the grammar corresponded to a possible “path” in the transition network. An input sentence was analyzed by attempting to match it with one of these paths, noting which specific instance of a wild card, such as <ATTRIBUTE>, was used in the match. Depending on the path taken and on the values of wild cards in the path, software was automatically created that was then used to make the appropriate database query or to carry out an appropriate command.6 In 1982, Hendrix left SRI to form Symantec, a company that planned to develop and market a natural language question-answering system based on semantic grammars such as LIFER. [Perhaps natural language processing (or the intended market) was not quite ready, because Symantec was later reorganized to market computer security and anti-virus software.] LIFER was used at SRI as the natural language component of a system called “LADDER” for accessing multiple, distributed databases.7 LADDER (an acronym for Language Access to Distributed Data with Error Recovery) translated the English query into a hypothetical database query that assumed a very simple database organization. Using a system called IDA (an acronym for Intelligent Data Access), that hypothetical query was transformed into a series of actual database queries that took into account the actual organization of the database. It also took account of syntactic and semantic knowledge to attempt to produce very efficient queries and to detect any erroneous updates to the database content. (More research on systems similar to IDA was performed in a joint program between Stanford University and SRI, named KBMS, an acronym for Knowlege Based Management System, with support from DARPA.) Consistent with DARPA’s focus on military applications, LADDER was able to answer questions about naval ships using information about ship sizes, types, locations, and so on from various databases. Some sample interactions with an early version of LADDER are shown in Fig. 19.3. Note the ability of the system to correct spelling errors, to deal with incomplete questions, and to accept paraphrases.8 19.2.2 CHAT-80 Between 1979 and 1982, Fernando Pereira (1952– ) and David H. D. Warren (circa 1950– ) developed a system called CHAT-80 at the University of Edinburgh as part of Pereira’s Ph.D. dissertation there. CHAT-80 was able to answer rather complex questions, posed in English, about a database of geographical facts. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 315
- 316. 19 Understanding Queries and Signals Figure 19.3: Sample interactions with LADDER. (Used with permission of SRI International.) According to Pereira’s dissertation,9 work on CHAT-80 started as “an attempt to clarify and improve some previous NL work of Colmerauer.” CHAT-80 was written in PROLOG, the logic-based programming language developed originally by Alain Colmerauer. In fact, the grammar used by CHAT-80 consisted of logical formulas stated in the PROLOG language. For example, sentence(s(NP,VP), S0,S) :- noun phrase(NP, N, S0,S1), 316 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 317. 19.2 Natural Language Access to Computer Systems verb phrase(VP, N, S1,S) is CHAT-80’s way of stating that “there is a sentence between points S0 and S in a string (of words) if there is a noun phrase with number N (that is, singular or plural) between points S0 and S1, and a verb phrase with number N between points S1 and S.” Grammars defined by PROLOG clauses of this kind are called Definite Clause Grammars (DCGs). Several clauses of this sort were used by CHAT-80 to parse English sentences. The actual parsing was done by the PROLOG program consisting of these clauses. In CHAT-80, computation of the meaning (that is, the semantics) of an English query was guided by the syntactic structure of the query (as computed by the PROLOG program) and was expressed as a logical formula. This formula was then transformed into the individual queries of the database needed to answer the original question.10 (For information about how to get a running version of CHAT-80, see http://www.cis.upenn.edu/∼pereira/oldies.html.) Here are a few examples (from Chapter 5 of Pereira’s dissertation) of queries that CHAT-80 was able to answer: Q: What is the capital of Upper Volta? A: Ouagadougou Q: Which country’s capital is London? A: united kingdom Q: What is the ocean that borders African countries and that borders Asian countries? A: indian ocean Q: What are the capitals of the countries bordering the Baltic? A: denmark:copenhagen; east germany:east berlin; finland:helsinki; poland:warsaw; soviet union:moscow; sweden:stockholm; west germany:bonn Q: What is the total area of countries south of the Equator and not in Australasia? A: 10,228 ksqmiles Q: What are the continents no country in which contains more than two cities whose population exceeds 1 million? A: africa, antarctica, australasia Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 317
- 318. 19 Understanding Queries and Signals Q: Which country bordering the Mediterranean borders a country that is bordered by a country whose population exceeds the population of India? A: turkey Although these examples indicate rather impressive performance, CHAT-80s abilities were constrained by its limited vocabulary and grammar. These limitations are described in detail in Pereira’s dissertation. 19.2.3 Transportable Natural Language Query Systems As I have described it, CHAT-80 was implemented as a system for querying a database of geographical facts. However, since much of its design was not specific to geography, it could rather easily be modified to be able to deal with other databases. CHAT-80 was just one of several query systems that were “transportable” in the sense that they could be adapted to serve as natural language front ends to a variety of different databases. Other such systems were ASK developed at Caltech,11 EUFID developed at SDC,12 IRUS developed at BBN,13 LDC-1 developed at Duke University,14 NLP-DBAP developed at Bell Laboratories,15 and TEAM developed at SRI.16 Since I know more about TEAM than I do about the others, I’ll say a few things about it as representative of its class. TEAM (an acronym for Transportable English Database Access Medium) was supported by DARPA and was designed to acquire information about a database from a database administrator and to interpret and answer questions of the database that are posed in a subset of English appropriate for that database. TEAM, like many other transportable systems, was built so that the information needed to adapt it to a new database and its corresponding subject matter could be acquired from an expert on that database even though he or she might know nothing about natural language interfaces. To illustrate the operation of TEAM, its designers used a database consisting of four “files” (or “relations”) of geographic data. Partial versions of these files are shown in Fig. 19.4. I’ll trace through some of the steps TEAM used to answer the query “Show each continent’s highest peak.” TEAM used a subsystem called DIALOGIC17 to convert the English query into a logical expression. Within DIALOGIC, a subsystem based on DIAMOND18 performed syntactic analysis using the DIAGRAM grammar.19 The highest scoring parse tree is shown in Fig. 19.5. Based on this parse tree and knowledge about the concepts used in the database, a semantic analysis system converted the query into the following logical expression (here restated in an English-like form for better understandability): 318 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 319. 19.3 HASP/SIAP Figure 19.4: Files used in a TEAM database. (Used with permission of SRI International.) FOR EVERY CONTINENT WHAT IS EACH PEAK SUCH THAT THE PEAK IS THE HIGHEST PEAK SUCH THAT THE CONTINENT IS CONTINENT OF THE PEAK? TEAM then used its knowledge about the structure of the database and about how components of this logical expression are associated with relations in the database to generate the actual database query and construct an answer. 19.3 HASP/SIAP In 1972, while Larry Roberts was still the Director of IPTO, he asked Ed Feigenbaum at Stanford to think about applying the AI ideas so successfully used in DENDRAL to the problem of identifying and tracking ships and submarines in the ocean using acoustic data from concealed hydrophone arrays. Some of the acoustic data picked up by the hydrophone arrays come from rotating shafts and propellors and reciprocating machinery on board ships. Different ships emit sounds with their own characteristic identifying fundamental frequencies and harmonics. Human specialists who analyze this sort of surveillance data look at the sonogram displays of ocean sounds and, by matching sound spectra to stored references, attempt to identify and locate ships that might be present (if any). Making these decisions often requires using information not present in the signals themselves, information such as Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 319
- 320. 19 Understanding Queries and Signals Figure 19.5: A parse tree for “Show each continent’s highest peak.” (Used with permission of SRI International.) reports from other sensor arrays, intelligence reports, and general knowledge about the characteristics of ships and common sea lanes. The analysis problem is complicated by several factors:20 The background noise from distant ships is mixed with storm-induced and biological noises. Sound paths to the arrays vary with diurnal and seasonal cycles. Arrival of sound energy over several paths may suddenly shift to no arrivals at all, or arrivals only of portions of vessel radiation. Sound from one source can appear to arrive from many directions at once. Characteristics of the receivers can also cause sound from different bearings to mix, appearing to come from a single location. Finally, the submarine targets of most interest are very quiet and secretive. 320 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 321. 19.3 HASP/SIAP Supported by DARPA, work on this problem began in 1973 at Systems Control Technology, Inc. (SCI), a Palo Alto company with expertise in this area that could work on classified military projects. (SCI was later acquired by British Petroleum.) Feigenbaum, and his colleagues at SCI, soon realized that the “generate-and-test” strategy of DENDRAL would not work for the problem of ocean surveillance because there was no “legal move generator” that could produce candidate ship positions and their tracks given the surveillance data. However, noting that the overall analysis problem could be divided into levels similar to those used in the Blackboard architecture of HEARSAY-II (a system shown to be good at dealing with signals in noise), the team thought that something similar would work for their problem. The team developed a system called HASP (an acronym for Heuristic Adaptive Surveillance Program) based on the Blackboard model. Follow-on work that would process actual ocean data began at SCI with SIAP (an acronym for Surveillance Integration Automation Program) in 1976. I’ll give a brief description of the HASP/SIAP system design and then summarize how it performed. The top level of the Blackboard was a “situation board” – a symbolic model of the unfolding ocean situation, built and maintained by the program. It described all the ships hypothesized to be out there with a confidence level associated with each of them. Just below the situation board level was a level containing the individual hypothesized vessels. Each vessel element had information about its class, location, current speed, course, and destination, each with a confidence weighting. Below the vessel level was a level containing hypothesized sound sources: engines, shafts, propellers and so on with their locations and confidence weightings. Spectral features abstracted from the acoustic data were at the lowest level. The levels were linked by knowledge sources (KSs) that were capable of inferring that if certain elements were suspected to be present at one level then other elements could be inferred to be present at another level (or if they were already present at that level, their confidence could be adjusted). Just as in HEARSAY-II, the links could span multiple levels and make inferences upward, downward, or within a level. An inference caused by one KS might allow another KS to draw an additional inference, and so on in cascade, until all relevant information had been used. In this manner, new information could be assimilated and expectations concerning possible future events could be formulated. One type of KS was composed of IF–THEN rules. (Other types were used also.) For example, here is an IF–THEN rule (translated into English for readability) that acted within the source level: IF: a source was lost due to fade-out in the near-past, and a similar source started up in another frequency, and the locations of the two sources are relatively close, Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 321
- 322. 19 Understanding Queries and Signals THEN: they are the same source with confidence of 3. HASP/SIAP had several kinds of knowledge sources, each represented in a way appropriate to the level(s) involved. Some KSs were based on information about the environment, such as common shipping lanes, location of arrays, and known maneuver areas. Others had information about vessels and vessel types, their speeds, component parts, acoustic characteristics, home bases, and so on. In addition to KSs dealing with knowledge appropriate to the various levels, there were “meta” KSs that had information about how to use other KSs. Figure 19.6: A network structure linking data at different levels. (Illustration from H. Penny Nii, Edward A. Feigenbaum, John J. Anton, and A. J. Rockmore, “Signal-to-Symbol Transformation: HASP/SIAP Case Study,” AI Magazine, Vol. 3, No. 2, p. 26, Figure 2, c 1982, Association for the Advancement of Artificial Intelligence. Used with permission.) The actions of the KSs in linking information at the various levels can be represented as a network, such as the one shown schematically in Fig. 19.6. At the end of an analysis session, when all KSs have had a chance to participate and the action dies down, the resulting network is called the “current best hypothesis” (CBH) about the current ocean situation. Here is a partial sample (translated into English) of how a CBH for a particular run of HASP/SIAP might be described:21 The class of Vessel-l, located in the vicinity of Latitude 37.3 and 322 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 323. 19.3 NOTES Longitude 123.1 at time day 2, 4 hours, 55 minutes, can be either Cherry, Iris, Tulip, or Poppy class. Two distinct acoustic sources, supported by respective harmonic sets, have been identified for Vessel-l. Source-l could be due to a shaft or propeller of vessel class Cherry or Poppy. Similar source possibilities exist for Source-5. These two sources were assimilated into Vessel-l because of the possibility of a known mechanical ratio that exists between the two sources. The MITRE Corporation conducted several experiments to compare the performance of HASP/SIAP against that of two expert sonar analysts. In one of these experiments, MITRE concluded that “HASP/SIAP has been shown to perform well on ocean derived data. . . For this restricted ocean scene, the program is not confused by extraneous data and gives results comparable to an expert analyst.” In another experiment, it concluded that “HASP/SIAP understood the ocean scene more thoroughly than the second analyst and as well as the first analyst. . . The program can work effectively with more than one acoustic array. SIAP classified an ocean scene over a three hour time period indicating the plausibility of SIAP efficacy in an evolving ocean situation.” The third experiment led to the conclusions that “with the exception that the SIAP program obtained significantly more contacts than the human analysts, the descriptions of the ocean scene are very similar.” Moreover, “SIAP can perform vessel classification in increasingly difficult ocean scenes without large increases in the use of computer resources.”22 As mentioned earlier, the Blackboard model has been applied in several other areas as well. Examples include protein crystallographic analysis,23 image understanding,24 and dialog comprehension.25 Interestingly, the Blackboard architecture has impacts beyond technology. Donald Norman, a cognitive psychologist, has said that HEARSAY-II has been a source of ideas for theoretical psychology and that it fulfills his “intuitions about the form of a general cognitive processing structure.”26 Also, as I’ll mention in a later chapter, several models of the neocortex involve interacting layers resembling both the form and the mechanisms of Blackboard systems. Notes 1. Lawrence G. Roberts, “Expanding AI Research and Founding Arpanet,” in Thomas C. Bartee (ed.), Expert Systems and Artificial Intelligence: Applications and Management, pp. 229–230, Indianapolis, IN: Howard W. Sams & Co., 1988. [309] 2. C. Cordell Green, “AI During IPTO’s Middle Years,” ibid, pp. 238–240. [309] 3. J. C. R. Licklider, “The Early Years: Founding IPTO,” ibid, pp. 225–226. [310] 4. Licklider letter in my file. [310] 5. J. C. R. Licklider, op. cit., p. 226. [311] Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 323
- 324. 19 NOTES 6. For technical details about LIFER, see Gary G. Hendrix, “LIFER: A Natural Language Interface Facility,” SRI AI Center Technical Note 135, December 1976 (available online at http://www.ai.sri.com/pubs/files/1414.pdf); Gary G. Hendrix, “The LIFER Manual: A Guide to Building Practical Natural Language Interfaces,” SRI AI Center Technical Note 138, February 1977 (available online at http://www.ai.sri.com/pubs/files/749.pdf); and Gary G. Hendrix, “Human Engineering for Applied Natural Language Processing,” Proceedings of the 5th IJCAI, pp. 183–191, 1977 (which also appeared as SRI AI Center Technical Note 139, available online at http://www.ai.sri.com/pubs/files/748.pdf). [315] 7. Earl D. Sacerdoti, “Language Access to Distributed Data with Error Recovery,” Proceedings of the 5th IJCAI, pp. 196–202, 1977, and reprinted as SRI AI Center Technical Note 140, February 1977 (available online at http://www.ai.sri.com/pubs/files/747.pdf); Earl D. Sacerdoti, “A LADDER User’s Guide (Revised),” SRI AI Center Technical Note 163R, March 1980 (available online at http://www.ai.sri.com/pubs/files/735.pdf); and Gary G. Hendrix et al. “Developing a Natural Language Interface to Complex Data,” ACM Transactions on Database Systems, Vol. 3, No. 2, pp. 105-147, June 1978 (available online as SRI AI Center Technical Note 152, August 1977, at http://www.ai.sri.com/pubs/files/741.pdf). [315] 8. For a more extensive interaction with a later version of LADDER, see Appendix A of Earl D. Sacerdoti, “A LADDER User’s Guide (Revised),” SRI AI Center Technical Note 163R, March 1980. [315] 9. Fernando Pereira, “Logic for Natural Language Analysis”, Ph.D. dissertation, University of Edinburgh, 1982. A slightly revised version of the dissertation was published as Technical Note 275 of the SRI AI Center and is available online at http://www.ai.sri.com/pubs/files/669.pdf. [316] 10. Readers interested in the details of these rather technical processes might refer to Pereira’s dissertation or to David H. D. Warren and Fernando Pereira, “An Efficient Easily Adaptable System for Interpreting Natural Language Queries,” Computational Linguistics, Vol. 8 , Nos. 3–4, pp. 110–122, July–December 1982. [317] 11. Bozena H. Thompson and Frederick B. Thompson, “Introducing ASK, A Simple Knowledgeable System,” Conference on Applied Natural Language Processing, pp. 17–24, 1983. Available online at http://ucrel.lancs.ac.uk/acl/A/A83/A83-1003.pdf. [318] 12. Marjorie Templeton and John Burger, “Problems in Natural Language Interface to DBMS with Examples from EUFID,” Proceedings of the First Conference on Applied Natural Language Processing, pp. 3–16, 1983. Available online at http://www.aclweb.org/anthology-new/A/A83/A83-1002.pdf. [318] 13. Madeleine Bates and Robert J. Bobrow, “A Transportable Natural Language Interface,” Proceedings of the 6th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 81–86, 1983. [318] 14. Bruce Ballard, John C. Lusth, and Nancy L. Tinkham, “LDC-1: A Transportable, Knowledge-Based Natural Language Processor for Office Environments,” ACM Transactions on Information Systems, Vol. 2, No. 1, pp. 1–25, January 1984. [318] 15. Jerrold M. Ginsparg, “A Robust Portable Natural Language Data Base Interface,” Conference on Applied Natural Language Processing, pp. 25–30, 1983. Available online at http://ucrel.lancs.ac.uk/acl/A/A83/A83-1004.pdf. [318] 16. Barbara J. Grosz et al., “TEAM: An Experiment in the Design of Transportable Natural-Language Interfaces,” Artificial Intelligence, Vol. 32, No. 2, pp. 173–243, May 1987. Available online as SRI Technical Note 356R, October 20, 1986, at http://www.ai.sri.com/pubs/files/601.pdf. [318] 17. Barbara Grosz et al., “DIALOGIC: A Core Natural-Language Processing System,” Proceedings of Ninth International Conference on Computational Linguistics, pp. 95–100, 1982. Available online at http://www.aclweb.org/anthology-new/C/C82/C82-1015.pdf. [318] 324 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 325. 19.3 NOTES 18. DIAMOND was developed at SRI by William Paxton and is described in Ann E. Robinson et al., “Interpreting Natural-Language Utterances in Dialogs About Tasks,” AI Center Technical Note 210, SRI International, March 1980. Available online at http://www.ai.sri.com/pubs/files/709.pdf. [318] 19. Jane J. Robinson, “DIAGRAM: A Grammar for Dialogs,” Communications of the ACM, Vol. 25, No. 1, pp. 27–47, January 1982. Available online as SRI AI Center Technical Note 205, February 1980, at http://www.ai.sri.com/pubs/files/712.pdf. [318] 20. H. Penny Nii, Edward A. Feigenbaum, John J. Anton, and A. J. Rockmore, “Signal-to-Symbol Transformation: HASP/SIAP Case Study,” AI Magazine, Vol. 3, No. 2, pp. 23–35, 1982. [320] 21. From ibid, p. 28. [322] 22. From ibid, p. 34. [323] 23. Robert S. Engelmore and H. Penny Nii, “A Knowledge-Based System for the Interpretation of Protein X-Ray Crystallographic Data,” Stanford Computer Science Department Technical Report CS-TR-77-589, 1977; available online at ftp://reports.stanford.edu/pub/cstr/reports/cs/tr/77/589/CS-TR-77-589.pdf. [323] 24. A. R. Hanson and E. M. Riseman, “VISIONS: A Computer System for Interpreting Scenes,” in A. Hanson and E. Riseman (eds.), Computer Vision Systems, pp. 303– 333, New York: Academic Press, 1978. [323] 25. W. C. Mann, “Design for Dialogue Comprehension,” in Proceedings of the 17th Annual Meeting of the Association of Computational Linguistics, pp. 83–84, La Jolla, CA, August 1979; available online at http://ucrel.lancs.ac.uk/acl/P/P79/P79-1020.pdf. [323] 26. Donald A. Norman, “Copycat Science or Does the Mind Really Work by Table Look-up?,” in R. Cole (ed.), Perception and Production of Fluent Speech, Chapter 12, Hillsdale, NJ: Lawrence Erlbaum Associates, Inc., 1980. [323] Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 325
- 326. 19 NOTES 326 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 327. 20.1 Chapter 20 Progress in Computer Vision Beginning around 1970 computer vision research grew into a highly developed subspecialty of AI, joining other specialized areas such as natural language processing, robotics, knowledge representation, and reasoning (to name just a few of them). In this chapter I’ll describe some of the important advances in computer vision during this period. Some of these were made in pursuit of specific applications in several fields such as aerial reconnaissance, cartography, robotics, medicine, document analysis, and surveillance.1 20.1 Beyond Line-Finding In an earlier chapter, I described some filtering techniques for enhancing image quality and for extracting edges and lines in images. But much more can be done to extract properties of a scene using specific information about the conditions under which images are obtained and general information about the properties of objects likely to be in the scene. 20.1.1 Shape from Shading In what has been called a “back-to-basics” movement, researchers began investigating how information about the physics and geometry of light reflection from surfaces could be used to reveal three-dimensional properties of a scene from a single two-dimensional image. A leader in this study was Berthold K. P. Horn (1943– ; Fig. 20.1). His MIT Ph.D. dissertation derived mathematical methods for determining the shape of an object from its shading.2 Just as humans perceive an appropriately shaded image of a circle Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 327
- 328. 20 Progress in Computer Vision as a sphere, a computer vision system can be made to do so also. Making it do so, using information about the reflective properties of surfaces and the geometry of the imaging process, is what Horn did. Figure 20.1: Berthold Horn (left) and a shaded circle (right). (Photograph courtesy of Berthold Horn.) The basic idea of Horn’s technique can be explained by referring to Fig. 20.2 in which an infinitesimal piece of surface receives illumination from a light source at an angle equal to i relative to the direction that points perpendicularly away from the surface piece. Suppose a light sensor (such as a TV camera), at an angle g relative to the direction of the light source and at an angle e relative to the direction of the surface, gathers the light reflected from the surface. The amount of light gathered from this surface patch depends on these three angles, the amount of illumination, and the reflectance properties of the surface. (Horn assumed what we would call a “matte” surface.) Because the amount of light gathered does vary in this manner, the image appears “shaded.” Under certain circumstances, and with quite a bit of mathematical manipulation, the direction of the surface can be calculated if the other quantities are known. Then, by knowing the direction for many, many infinitesimal pieces of surface, the overall shape of the surface can be calculated (under the assumption that the surface is relatively smooth with no abrupt discontinuities). Horn is now a professor of computer science and electrical engineering at MIT and continues to work on several topics related to computer vision. His thesis elicited a flurry of activity in the area of “shape from shading.”3 Several people extended the idea of shape from shading to attempt to calculate shape based on things other than shading, such as from multiple images (stereo), motion, texture, and contour. And, as we shall see in the next few pages, important work was done in extracting more than just the shape of objects. 328 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 329. 20.1 Beyond Line-Finding Figure 20.2: Light incident on and reflected by a small piece of a surface. (Il- lustration used with permission of Berthold Horn.) 20.1.2 The 21 2 -D Sketch Even though a viewer sees only a two-dimensional image of a three-dimensional scene, David Marr (augmenting Horn’s ideas) observed that, nevertheless, a viewer is able to infer (and thus perceive) from image shading and other depth cues some of the scene’s three-dimensional attributes, such as surface shapes, shapes occluding other shapes, abrupt changes between smooth surfaces, and other depth information. Marr called the representation of these attributes a “21 2 -D sketch” (because it was not fully three dimensional). According to Marr’s theory of vision (described in his book4 ), the next step of visual processing, after producing the primal sketch (see p. 180) of blobs and edges, is to produce this 21 2 -D sketch. An example sketch is shown in Fig. 20.3 in which arrows pointing perpendicularly away from surfaces are superimposed on the primal sketch of an image from which they are inferred. Finally, according to Marr, the information in the 21 2 -D sketch, along with stored information about object shapes, would be used to locate specific objects in the image and thus produce a 3-D model of the scene. I’ll describe what he had to say about that process shortly. 20.1.3 Intrinsic Images Two researchers at SRI, Jay Martin Tenenbaum (1943– ; Fig. 2.4) and Harry Barrow (recently relocated from Edinburgh), developed some image-processing techniques quite similar to those used in producing the 21 2 -D sketch.5 They Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 329
- 330. 20 Progress in Computer Vision Figure 20.3: A 21 2 -D sketch. (From David Marr and H. K. Nishihara, “Rep- resentation and Recognition of the Spatial Organization of Three-Dimensional Shapes,” Proceedings of the Royal Society of London, Series B, Biological Sci- ences, Vol. 200, No. 1140, p. 274, February 23, 1978.) noted that the intensity value at each pixel of an image resulted from a tangled combination of several factors, including properties of the ambient illumination and reflective and geometric properties of objects in the scene. They thought that these factors could be untangled to recover important three-dimensional information about the scene. Barrow and Tenenbaum proposed that each of these factors (all of which influenced intensity) could be represented by imaginary images that they called “intrinsic images.” These images were to consist of a grid of “pixels” overlaying a projection of the scene and in registration with the intensity 330 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 331. 20.1 Beyond Line-Finding Figure 20.4: Jay Martin Tenenbaum (left) and Harry Barrow (right). (Pho- tographs courtesy of J. Martin Tenenbaum and of Harry Barrow.) image. One intrinsic image, for example, was an illumination image. It consisted of pixels whose values were the amounts of illumination falling on the pixels of the projected scene. These values, of course, were not known, but Barrow and Tenenbaum proposed that they could be estimated from the intensity image and from the other intrinsic images. As examples, I show a set of such intrinsic images in Fig. 20.5. The actual image of intensity values is shown at the top. The known value of a pixel in that image depends on the unknown values of pixels in the intrinsic images below. In fact, the values of the pixels in all of the images, intrinsic and actual, are interdependent. The arrows in the figure reflect that fact. (There should also be some arrows going up.) Based on the values of pixels in some of the images, the values of others can be computed by using known physical relationships, constraints among the images, and other reasonable assumptions. These values, in turn, allow the computation of others. In essence, these computations “propagate” pixel values throughout the set of intrinsic images (much like how levels in the Blackboard architecture affect other levels). As Barrow and Tenenbaum later summarized their method, “We envisaged this recovery process as a set of interacting parallel local computations, more like solving a system of simultaneous equations by relaxation than like a feed forward sequence of stages.”6 Barrow and Tenenbaum also used some of their ideas about intrinsic images to work on the problem of interpreting line drawings as three-dimensional surfaces.7 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 331
- 332. 20 Progress in Computer Vision Figure 20.5: Intrinsic images. (Used with permission of Harry Barrow and Jay M. Tenenbaum.) Barrow and Tenenbaum intended their work to be useful not only in computer vision but also as a potential model of “precognitive” vision processes in humans. However, in a 1993 “retrospective” about their work they wrote8 Despite the maturity of computational vision and the rapid developments in neural systems, we still have a long way to go before we can come close to our goal of understanding visual 332 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 333. 20.2 Finding Objects in Scenes perception. To do so we will need to draw upon what we have learned in many fields, including neuroscience, neural networks, experimental psychology and computational vision. 20.2 Finding Objects in Scenes 20.2.1 Reasoning about Scenes Even before the development of shape-from-shading and other methods for recovering depth information from scenes, a number of researchers had worked on methods for finding objects in scenes. I described many of these techniques in Section 9.3. During the early 1970s, Thomas Garvey completed a Stanford Ph.D. thesis on a system for locating objects, such as desks, chairs, and wastebaskets, in images of office scenes.9 As Garvey wrote in his summary, The system uses information about the appearances of objects, about their interrelationships, and about available sensors to produce a plan for locating specified objects in images of room scenes. In related work, Barrow and Tenenbaum developed a system, called MSYS, for reasoning about scenes “in which knowledge sources compete and cooperate until a consistent explanation of the scene emerges by consensus.”10 MSYS analyzed images of office scenes and attempted to find the most likely interpretation for the regions in an image (desk top, back of chair, floor, doorway, and so on) given a number of candidate interpretations and their probabilities. Knowing relationships between regions (such as “chair backs are usually adjacent to chair seats”), MSYS tried to find the most likely overall set of region interpretations. An example of a scene considered by MSYS is shown in Fig. 20.6. Some of the regions in the scene have been detected and labeled with possible interpretations. As Barrow and Tenenbaum wrote, MSYS’s reasoning might proceed as follows: Regions PIC, WBSKT, and CBACK cannot be WALL or DOOR, because their brightnesses are much less than that along the top edge of the image vertically above them, which violates [knowledge about the brightness of walls and doors]. Consequently, region PIC must be the PICTURE, WBSKT must be WASTEBASKET, and CBACK must be CHAIRBACK. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 333
- 334. 20 Progress in Computer Vision Figure 20.6: An MSYS scene with some regions detected and labeled. (Illustra- tion used with permission of SRI International.) Region LWALL and RWALL must then be WALL, since they are adjacent to region PIC, and DOOR cannot be adjacent to PICTURE. Region DR cannot be WALL because all regions labeled WALL are required to have the same brightness. Therefore, region DR must be DOOR. 334 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 335. 20.2 Finding Objects in Scenes 20.2.2 Using Templates and Models Much of the early work on object recognition was based on using object “templates” that could be matched against images. Martin A. Fischler and Robert A. Elschlager elaborated this idea by using “stretchable templates” that permitted more powerful matching techniques. They used these to find objects such as faces or particular terrain features in photographs containing such objects.11 The process depended on having a general representation for the object being sought and then a process for matching that representation against the photograph. Their representations were based on breaking an object down into a number of primitive parts and “specifying an allowable range of spatial relations which these ‘primitive parts’ must satisfy for the object to be present.” For the object to be present in a picture, “it is required that [the] primitives occur (or at least that some significant subset of them occurs), and also that they occur within a certain spatial relationship one to the other. . . ” As Fischler and Elschlager pointed out, it is usually the case that determining whether or not some of the parts occur depends on whether or not the whole object occurs, and vice versa. The main contribution of their paper was the development of a dynamic-programming-style method for dealing with this circularity. Earlier I had described David Marr’s work on processes for producing a primal sketch and a 21 2 -D sketch. These were the first two stages in Marr’s theory of vision. He argued that these stages could uncover important shape information without specific knowledge of the shapes of objects likely to be in a scene. He had written:12 Most early visual processes extract information about the visible surfaces directly, without particular regard to whether they happen to be part of a horse, or a man, or a tree. . . . As for the question of what additional knowledge should be brought to bear, general knowledge must be enough – general knowledge embedded in the early visual processes as general constraints, together with the geometrical consequences of the fact that the surfaces co-exist in three-dimensional space. Specific knowledge about shapes, he argued, should be utilized in a third stage. It is this stage that uses three-dimensional models of objects. He proposed using a hierarchy of models in which a gross model is decomposed into subparts and these into subsubparts and so on. For example, the shape of a human might be modeled as in Fig. 20.7. Each box corresponds to a 3-D model and its submodel. On the left side of the box is an axis-oriented model; on the right side is how that model is represented as submodels. (Directions of the axes can be adjusted to fit matching parts of the image.) In this third stage, comparing models of this sort with shape information and other 3-D information contained in the 21 2 -D sketch helps to identify and Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 335
- 336. 20 Progress in Computer Vision Figure 20.7: An example of one of Marr’s 3-D model hierarchies. (From David Marr, Vision, San Francisco: W. H. Freeman and Co., p. 306, 1982.) locate objects in a scene. For Marr, vision was “the process of discovering from images what is present in the world and where it is.”13 Marr was not the first to suggest the use of cylinders as models of parts of objects. In a 1971 IEEE conference paper, Thomas O. Binford (1936– ) introduced the idea of “generalized cylinders” (sometimes called “generalized cones”).14 A later paper defined them as follows: “A generalized cone is defined by a planar cross section, a space curve spine, and a sweeping rule. It represents the volume swept out by the cross section [not necessarily a circular one] as it is translated along [an axis called a spine], held at some constant angle to the spine, and transformed according to the sweeping rule.”15 Binford had several Stanford Ph.D. students who used models to help identify objects in scenes. Of these I might mention Gerald J. Agin,16 Ramakant Nevatia,17 and Rodney A. Brooks (1954– ),18 all of whom contributed to what came to be called “model-based vision.” (Brooks later became a professor at MIT, where he worked on other topics. His subsequent work will be discussed later.) Brooks’s ACRONYM system19 used generalized cones to model several different kinds of objects. ACRONYM used these models to help identify and locate objects in images. Some examples of the kinds of generalized cones that can be used as building blocks of models and model objects are shown in Fig. 20.8. Other views regarding what vision is all about competed with those of Marr and others who were attempting to use vision to reconstruct entire scenes. Some, especially those involved in robotics, claimed that the purpose of vision was to perceive just what was required to guide action. Many of the 336 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 337. 20.2 Finding Objects in Scenes Figure 20.8: Primitive generalized cones and piston models constructed from generalized cones. (From Rodney A. Brooks, “Symbolic Reasoning among 3-D Models and 2-D Images,” Artificial Intelligence, Vol. 17, Nos. 1–3, pp. 285–348, 1981.) vision routines in Shakey were embedded in its action programs. Professor Yiannis Aloimonos at the University of Maryland is one of the researchers advocating this “purposive” or “interactive” approach. He claims that the goal of vision is action. When vision is “considered in conjunction with action, it becomes easier.” He goes on to explain that “the descriptions of space-time that the system needs to derive are not general purpose, but are purposive. This means that these descriptions are good for restricted sets of tasks, such as tasks related to navigation, manipulation and recognition.”20 In the neuroscience community, to which Marr wanted to make a contribution, there were Patricia S. Churchland, V. S. Ramachandran, and Terrence J. Sejnowski, who later wrote “What is vision for? Is a perfect internal recreation of the three-dimensional world really necessary? Biological and computational answers to these questions lead to a conception of vision quite different from pure vision [as advocated by Marr]. Interactive vision. . . includes vision with Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 337
- 338. 20 Progress in Computer Vision other sensory systems as partners in helping to guide actions.”21 In any case, models still play an important role in computer vision. (However, one prominent vision researcher told me that the “residue of model-based vision is close to zero,”22 and another told me that “most current robotic systems use vision hacks” instead of general-purpose, science-based scene-analysis methods.23 ) 20.3 DARPA’s Image Understanding Program Much of the computer vision work in the United States was being funded by DARPA, and there were concerns among vision researchers (as always) about continuing support. Tenenbaum recalls attending a DARPA meeting in 1974 where the future of computer vision research was being discussed. The program officer monitoring DARPA-supported vision work, Air Force Major David L. Carlstrom, was at the meeting and was interested in pulling together the various efforts in the field. Because DARPA had been supporting work in this area for some years, Carlstrom needed a new name that would indicate that DARPA was starting something new. Tenenbaum told me that he recommended to Carlstrom that the new initiative be called “the image understanding program.”24 (Recall that there was already an ongoing DARPA-supported effort in speech understanding, so the phrase sounded “DARPA-friendly.”) In 1976, DARPA launched its Image Understanding (IU) program. It grew to be a major effort composed of the leading research laboratories doing work in this area as well as “teams” pairing a university with a company. The individual labs participating were those at MIT, Stanford, University of Rochester, SRI, and Honeywell. The university/industry teams were USC–Hughes Research Laboratories, University of Maryland–Westinghouse, Inc., Purdue University–Honeywell, Inc., and CMU–Control Data Corporation. Regular workshops were held to report progress. The proceedings of one held in April 1977 stated the goals of the program: “The Image Understanding Program is planned to be a five year research effort to develop the technology required for automatic and semiautomatic interpretation and analysis of military photographs and related images.”25 DARPA’s ultimate goal for the IU program was well captured by the illustration on the cover of that proceedings, shown in Fig. 20.9. As the diagram implies, military commanders would like computer vision systems to be able to analyze a photograph and to produce a written description of its important components and their relationships. Some of the computer vision research that I have already described, such as work on the 21 2 -D sketch, intrinsic images, generalized cylinders, and ACRONYM, was supported by the IU Program. But there was always some 338 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 339. 20.3 DARPA’s Image Understanding Program Figure 20.9: An illustration of IU goals. (Illustration used with permission of SAIC.) tension between DARPA’s goals and those of people doing computer vision research. DARPA wanted the program to produce “field-able” systems. J. C. R. Licklider emphasized this point at a preliminary IU workshop in March 1975:26 At the end of the five year period the technology developed must be in a state in which it can be utilized by the DoD components to solve their specific problems without requiring a significant research effort to figure our how to apply the technology to the specific problems. For this reason, the program must result in a demonstration at the end of the five year period that an important DoD problem has been solved. Air Force Major Larry Druffel at DARPA assumed leadership of the IU program in 1978. In November 1978, he advised “The prudent approach is to consolidate those techniques which are sufficiently mature to transfer to DoD agencies.”27 By 1979, the program’s goals had expanded to include cartography and mapping. A “memorandum of understanding” (MOU) between DARPA and the Defense Mapping Agency (DMA) was concluded to support automatic mapping efforts through the development of a DARPA/DMA “testbed.” In November 1979, Druffel wrote28 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 339
- 340. 20 Progress in Computer Vision Plans are progressing for a demonstration system to evaluate the maturity of IU technology by automating mapping, charting, and geodesy functions. While focussing on specific cartographic photointerpretation functions, the system should offer the entire image exploitation community an opportunity to assess the future application of Image Understanding methodologies to their specific problem. The “five-year” program did not end in 1981. It continued under the DARPA leadership of Navy Commander Ron Ohlander, Air Force Lt. Col. Robert L. Simpson Jr., and others until approximately 2001. In 1985 Simpson summarized some of its accomplishments:29 Originally conceived as a five year program in 1975 by Lt. Col. David Carlstrom, the first several years of IU established the strong base of low-level vision techniques and knowledge-based subsystems that began to differentiate computer vision from what is usually called “image processing.” In the late 1970s and early 1980s, under the direction of Lt. Col. Larry Druffel, the program saw the development of model-based vision systems such as ACRONYM and demonstration of IU techniques in more meaningful concept demonstrations such as the DARPA/DMA image understanding testbed. These demonstrations and their potential for future military use warranted the continuation of the IU program beyond its initial five year lifespan. Under Cmd. Ron Ohlander, IU technology continued to mature to the point that the DARPA Strategic Computing Program could justify a major application, the autonomous land vehicle. As Ohlander said, the IU program was extended beyond its projected five-year lifetime. It is said that even as early as 1984, DARPA had spent over $4 million on this effort.30 One potential application was computer vision for robot-controlled military vehicles – a component of DARPA’s “Strategic Computing” program. I’ll describe that application and others in more detail in later chapters. As a growing subspecialty of artificial intelligence, papers on computer vision began to appear in new journals devoted to the subject including Computer Vision and Image Understanding and IEEE Transactions on Pattern Analysis and Machine Intelligence. The field’s textbooks around this time included Pattern Classification and Scene Analysis31 and two books titled Computer Vision.32 340 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 341. 20.3 NOTES Notes 1. For an extensive list of computer vision applications see the CVonline Web site at http://homepages.inf.ed.ac.uk/rbf/CVonline/applic.htm. [327] 2. Berthold K. P. Horn, “Shape from Shading: A Method for Obtaining the Shape of a Smooth Opaque Object from One View,” MIT Department of Electrical Engineering Ph.D. thesis, MIT Artificial Intelligence Laboratory Technical Report 232, November 1970; available online at http://people.csail.mit.edu/bkph/AIM/AITR-232-OCR-OPT.pdf. In his thesis, Horn credits Thomas Rindfleisch’s 1966 work on using image brightness in studies of lunar topography. [327] 3. For a modern discussion of the problem, see, for example, Emmanuel Prados and Olivier Faugeras, “Shape from Shading,” in N. Paragios, Y. Chen, and O. Faugeras (eds.), Handbook of Mathematical Models in Computer Vision, pp. 375–388, New York: Springer-Verlag, 2006; available online at http://perception.inrialpes.fr/Publications/2006/PF06a/chapter-prados-faugeras.pdf. [328] 4. David Marr, Vision: A Computational Investigation into the Human Representation and Processing of Visual Information, San Francisco: W. H. Freeman and Co., 1982. [329] 5. Harry G. Barrow and Jay Martin Tenenbaum, “Recovering Intrinsic Scene Characteristics from Images,” in A. Hanson and E. Riseman (eds.), Computer Vision Systems, pp. 3–26, New York: Academic Press, 1978. Available online at http://web.mit.edu/cocosci/Papers/Barrow-Tenenbaum78.pdf and at http://www.ai.sri.com/pubs/files/737.pdf. [329] 6. Harry G. Barrow and Jay Martin Tenenbaum, “Retrospective on ‘Interpreting Line Drawings as Three-Dimensional Surfaces,’” Artificial Intelligence, Vol. 59, Nos. 1–2, pp. 71–80, 1993. [331] 7. Harry G. Barrow and Jay Martin Tenenbaum, “Interpreting Line Drawings as Three-Dimensional Surfaces,” Artificial Intelligence, Vol. 17, pp. 75–116, 1981. Available online at http://web.mit.edu/cocosci/Papers/Barrow-Tenenbaum81.pdf. [331] 8. Harry G. Barrow and Jay Martin Tenenbaum, “Retrospective on ‘Interpreting Line Drawings as Three-Dimensional Surfaces,’” Artificial Intelligence, Vol. 59, Nos. 1–2, pp. 71–80, 1993. [332] 9. Thomas D. Garvey, “Perceptual Strategies for Purposive Vision,” Stanford University Ph.D. thesis, published as SRI International AI Center Technical Note 117, September 1976. Abstract available online at http://www.ai.sri.com/pub list/759. [333] 10. Harry G. Barrow and J. Martin Tenenbaum, “MSYS: A System for Reasoning about Scenes,” SRI International AI Center Technical Note 121, April 1976. Available online at http://www.ai.sri.com/pubs/files/757.pdf. [333] 11. Martin A. Fischler and Robert A. Elschlager, “The Representation and Matching of Pictorial Structures,” IEEE Transactions on Computers, Vol. C-22, No. 1, pp. 67–92, January 1973. [335] 12. David Marr, op. cit., pp. 272–4. [335] 13. David Marr, op. cit., pp. 23–60. [336] 14. Thomas O. Binford, “Visual Perception by Computer,” Proceedings of the IEEE Conference on Systems and Control, Miami FL, December 1971. [336] 15. Rodney A. Brooks, “Symbolic Reasoning among 3-D Models and 2-D Images,” Artificial Intelligence, Vol. 17, Nos. 1–3, pp. 285–348, 1981. [336] 16. Gerald J. Agin, “Representation and Description of Curved Objects,” Stanford University Ph.D. thesis, published as Stanford Artificial Intelligence Project Memo AIM-173, Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 341
- 342. 20 NOTES October 1972. See also Gerald J. Agin and Thomas O. Binford, “Computer Descriptions of Curved Objects,” Proceedings of the Third International Joint Conference on Artificial Intelligence, pp. 629–640, August 1973; later published as Gerald J. Agin and Thomas O. Binford, “Computer Descriptions of Curved Objects,” lEEE Transactions on Computers, Vol. 25, No. 4, April 1976. [336] 17. Ramakant Nevatia, “Structured Descriptions of Complex Curved Objects for Recognition and Visual Memory,” Stanford University Department of Electrical Engineering Ph.D. thesis, published as Stanford Artificial Intelligence Laboratory Memo AIM-250, October 1974. [336] 18. Rodney A. Brooks, “Symbolic Reasoning among 3-D Models and 2-D Images,” Stanford University Computer Science Department Ph.D. thesis, 1981, published as Stanford CS Department Report STAN-CS-81-861. Also published as Rodney A. Brooks, op. cit.. [336] 19. The system was first reported in Rodney A. Brooks, Russell Greiner, and Thomas O. Binford, “The ACRONYM Model-Based Vision System,” Proceedings of the Sixth International Joint Conference on Artificial Intelligence, pp. 105–113, Tokyo, 1979. A later revised version was reported in Brooks’s Artificial Intelligence paper just cited. [336] 20. From his Web page at http://www.cfar.umd.edu/∼yiannis/. [337] 21. Patricia S. Churchland, V. S. Ramachandran, and Terrence J. Sejnowski, “A Critique of Pure Vision,” in Christof Koch and Joel L. Davis (eds.), Large-Scale Neuronal Theories of the Brain, pp. 23–65, Cambridge, MA: MIT Press, 1994. Available online at http: //philosophy.ucsd.edu/faculty/pschurchland/papers/kochdavis94critiqueofpurevision.pdf. [338] 22. Martin A. Fischler, private communication, August 1, 2007. [338] 23. Jay Martin Tenenbaum, private communication, July 31, 2007. [338] 24. Private communication, July 31, 2007. [338] 25. Lee S. Bauman (ed.), Proceedings: Image Understanding Workshop, Science Applications, Inc., Report No. SAI-78-549-WA, April 1977. [338] 26. Quoted in the Foreword of the Proceedings: Image Understanding Workshop, published by Science Applications, Inc., May 1978. [339] 27. Quoted in the Foreword of the Proceedings: Image Understanding Workshop, published by Science Applications, Inc., November 1978. [339] 28. Quoted in the Foreword of the Proceedings: Image Understanding Workshop, published by Science Applications, Inc., November 1979. [339] 29. Quoted in the Foreword of the Proceedings: Image Understanding Workshop, published by Science Applications International Corporation, December 1985. [340] 30. Alex Roland with Philip Shiman, Strategic Computing: DARPA and the Quest for Machine Intelligence, p. 220, Cambridge, MA: MIT Press, 2002. [340] 31. Richard O. Duda and Peter E. Hart, Pattern Classification and Scene Analysis, New York: John Wiley and Sons, Inc., 1973. [340] 32. Michael Brady, Computer Vision, Amsterdam: North-Holland Publishing Co., 1981, and Dana H. Ballard and C. M. Brown, Computer Vision, New York: Prentice Hall, Inc., 1982. [340] 342 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 343. 21.0 Chapter 21 Boomtimes Even though the Mansfield Amendment and the Lighthill report caused difficulties for basic AI research during the 1970s, the promise of important applications sustained overall funding levels from both government and industry. Excitement, especially about expert systems, reached a peak during the mid-1980s. I think of the decade of roughly 1975–1985 as “boomtimes” for AI. Even though the boom was followed by a period of retrenchment, its accomplishments were many and important. It saw the founding in 1980 of the American Association for Artificial Intelligence (AAAI – now called the Association for the Advancement of Artificial Intelligence), with annual conferences, workshops, and symposia. (Figure 21.1 shows a scene from one of the many trade shows during this era.) Several other national and regional AI organizations were also formed. The Arpanet, which had its beginnings at a few research sites in the late 1960s, gradually evolved into the Internet, linking computers worldwide. Various versions of the LISP programming language coalesced into INTERLISP, which continued as the predominant language for both AI research and applications (although PROLOG was a popular competitor in Europe, Canada, and Japan). Researchers and students at MIT designed work-station-style computers, called Lisp machines, that ran LISP programs efficiently. Lisp Machines, Inc., and Symbolics were two companies that built and sold these machines. They enjoyed initial success but gradually lost out to other providers of workstations.1 Many other AI companies joined the expert systems companies and the Lisp machine companies. For example, in 1978 Earl Sacerdoti and Charles Rosen founded Machine Intelligence Company to market robot vision systems. In 1984 Cuthbert Hurd (1911–1996), who had earlier helped IBM develop its first computer, and David Warren founded Quintus, Inc., to market PROLOG Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 343
- 344. 21 Boomtimes systems. In 1984 Fritz Kunze, a graduate student at UC Berkeley, founded Franz, Inc., to market FranzLISP, a version of the LISP programming language.2 Lavish exhibits at trade shows associated with AI conferences charged the whole field with excitement. Membership in the AAAI rose from around 5,000 shortly after the society’s founding to a peak of 16,421 in 1987. (AAAI membership has since leveled off, after the boom, back to around 5,000.) Most of these new members – curious about what AI could do for them – came from industry and government agencies. Tutorials about various AI topics at both AAAI and IJCAI conferences were very well attended by people from industry wanting to learn about this newly important field. During the early 1980s, my colleagues in several departments at SRI, especially those working on Defense Department projects, were eager to get help from the SRI AI Center – of which I was the director at the time. Mainly, I thought, they wanted us to “sprinkle a little AI” on their proposed projects to make them more enticing to government sponsors. Figure 21.1: Scene from one of the AAAI trade shows during the 1980s. (Pho- tograph from Bruce B. Buchanan, “Some Recollections about the Early Days of AAAI,” AI Magazine, Vol. 26, No. 4, p. 14, c 2005 Association for the Advancement of Artificial Intelligence. Used with permission.) Reporting on this increasing interest in 1984, the science writer George Johnson wrote3 “We’ve built a better brain,” exclaimed a brochure for [an expert system called] TIMM, The Intelligent Machine Model: “Expert 344 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 345. 21.0 NOTES systems reduce waiting time, staffing requirements and bottlenecks caused by the limited availability of experts. Also, expert systems don’t get sick, resign, or take early retirement.” Other companies, such as IBM, Xerox, Texas Instruments, and Digital Equipment Corporation, were more conservative in their pronouncements. But the amplified voices of their salesmen, demonstrating various wares [in the 1984 AAAI exhibit hall], sounded at times like carnival barkers, or prophets proclaiming a new age. The boom continued with Japan’s “Fifth Generation Computer Systems” project. That project in turn helped DARPA justify its “Strategic Computing Initiative.” It also helped to provoke the formation of similar research efforts in Europe (such as the ALVEY Project in the United Kingdom and the European ESPRIT programme) as well as the formation of American industrial consortia for furthering advances in computer hardware. Assessments of some of AI’s difficulties and achievements, compared to some of its promises, led to the end of the boom in the late 1980s – causing what some called an “AI winter.” I’ll be describing all of these topics in subsequent chapters. Notes 1. By the way, the “iwhois” Web site (http://www.iwhois.com/oldest/) lists Symbolics as having the oldest registered “.com” domain name (registered on March 15, 1985.) [343] 2. See http://www.franz.com/about/company.history.lhtml. [344] 3. George Johnson, “Thinking about Thinking,” APF Reporter, Vol. 8, No. 1, 1984. Available online at http://www.aliciapatterson.org/APF0801/Johnson/Johnson.html. [344] Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 345
- 346. 21 NOTES 346 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 347. 21.0 Part V “New-Generation” Projects Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 347
- 348. 21 348 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 349. 22.1 Chapter 22 The Japanese Create a Stir 22.1 The Fifth-Generation Computer Systems Project In 1982, Japan’s Ministry of International Trade and Industry (MITI) launched a joint government and industry project to develop what they called “Fifth Generation Computer Systems” (FGCS). Its goal was to produce computers that could perform AI-style inferences from large data and knowledge bases and communicate with humans using natural language. As one of the reports about the project put it, “These systems are expected to have advanced capabilities of judgement based on inference and knowledge-base functions, and capabilities of flexible interaction through an intelligent interface function.”1 The phrase “Fifth Generation” was meant to emphasize dramatic progress beyond previous “generations” of computer technology. The first generation, developed during and after World War II, used vacuum tubes. Around 1959, transistors replaced vacuum tubes – giving rise to the second generation – although the transistors, like the vacuum tubes before them, were still connected to each other and to other circuit components using copper wires. During the 1960s, transistors and other components were fabricated on single silicon wafer “chips,” and the several chips comprising a computer were connected together by wires. Computers using this so-called small-scale integration (SSI) technology comprised the third generation. In the late 1970s, entire microprocessors could be put on a single chip using “very large-scale integration” (VLSI) technology – the fourth generation. The Japanese fifth generation, besides its sophisticated software, was to involve many parallel processors using “ultra large-scale integration” (ULSI). MITI planned to develop a prototype machine, in the form of what Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 349
- 350. 22 The Japanese Create a Stir computer scientists were beginning to call a “workstation,” which was to consist of several processors running in parallel and accessing multiple data and knowledge bases. PROLOG, the computer programming language based on logic, was to be the “machine language” for the system because the Japanese thought it would be well suited for natural language processing, expert reasoning, and the other AI applications they had in mind. Execution of a PROLOG statement involved logical inference, so the machine’s performance was to be measured in logical inferences per second (LIPS). In the early 1980s, computers were capable of performing around 100,000 LIPS. The Japanese thought they could speed that up by 1,000 times and more. Later in the project, because of difficulties of adapting PROLOG to run concurrently on many processors, a new logic-based language, GHC (for Guarded Horn Clauses), was developed that could run on multiple processing units. For work on FGCS, MITI set up a special institute called the “Institute for New Generation Computer Technology” (ICOT). Its Research Center, headed by Mr. Kazuhiro Fuchi (1936–2006; Fig. 22.1), was to carry out the basic research needed to develop a prototype system. According to its first-year progress report,2 the “Research Center started with forty top-level researchers from the Electrotechnical Laboratories (ETL), Nippon Telephone and Telegraph Public Corporation (NTT), and eight computer manufacturers.” The project had a ten-year plan: three years of initial research, four years of building intermediate subsystems, and a final three years to complete the prototype. In 1993, the project was extended for two years to disseminate FGCS technology. Koichi Furukawa (1942– ; Fig. 22.1), a Japanese computer scientist, was influential in ICOT’s decision to use PROLOG as the base language for their fifth-generation machine. Furukawa had spent a year at SRI during the 1970s, where he learned about PROLOG from Harry Barrow and others. Furukawa was impressed with the language and brought Alain Colmerauer’s interpreter for it (written in FORTRAN) back to Japan with him. He later joined ICOT, eventually becoming a Deputy Director. (He is now an emeritus professor at Keio University.) The architecture of the planned fifth-generation system is illustrated in Fig. 22.2. Various hardware modules for dealing with the knowledge base, inference, and interface functions were to be implemented using advanced chip technology. The hardware would be controlled with corresponding software modules, and interaction with the system would be through speech, natural language, and pictures. According to a set of slides by Mr. Shunichi Uchida summarizing the FGCS project,3 its total ten-year budget was U54.2 billion or approximately (at the 1990 exchange rate) $380 million. During this time, the project made advances in parallel processing, in computer architecture, and in developing various AI systems. Several 350 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 351. 22.1 The Fifth-Generation Computer Systems Project Figure 22.1: Kazuhiro Fuchi (left) and Koichi Furukawa (right). (Fuchi pho- tograph courtesy of Tohru Koyama. Furukawa photograph courtesy of Koichi Furukawa.) American and European visitors (especially PROLOG experts) participated in the project as ICOT visitors. Indeed, the Japanese invited international participation in the project. International conferences were held in Tokyo in 1984, 1988, and 1992.4 ICOT built a number of “parallel inference machines” (PIMs). The largest of these, named PIM/p, had 512 processing units.5 (See Fig. 22.3.) Several AI systems were developed to run on these machines. Among these were MGTP (an acronym for Model Generation Theorem Prover), a parallel theorem prover;6 MENDELS ZONE, a system for automatic program generation;7 and HELIC-II, a legal reasoning expert system.8 Many observers think that most of the results of the FGCS project are now of historical interest only. The software developed did not find notable applications. Improvements in the speed and power of commercial workstations (and even of personal computers) made these superior to the PIMs. Taking full advantage of the benefits of parallel processing proved difficult except for special problems susceptible to that style of computation. The development of graphical user interfaces (GUIs) during the late 1980s and 1990s provided acceptable methods for human–computer interaction – reducing (at least for a time) the need for AI-dependent natural language and speech understanding systems. One legacy of the project is the journal New Generation Computing, of which Koichi Furukawa was once editor-in-chief. A “Museum” Web page for the FGCS project is maintained at http://www.icot.or.jp/ARCHIVE/HomePage-E.html. The page contains links to several ICOT publications, software, and other information. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 351
- 352. 22 The Japanese Create a Stir Figure 22.2: Fifth-generation system architecture. (Illustration used with per- mission of Edward Feigenbaum.) A 1993 article, with contributions from several knowledgeable people, reflected about the project.9 Evan Tick, one of the contributors who had spent time at ICOT, had this to say:10 . . . I highly respect the contribution made by the FGCS project in the academic development of the field of symbolic processing, notably implementation and theory in logic programming, constraint and concurrent languages, and deductive and object-oriented databases. In my specific area of parallel logic programming languages, architectures, and implementations, ICOT made major contributions, but perhaps the mixed schedule of advanced technology transfer and basic research was ill advised. 352 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 353. 22.2 The Fifth-Generation Computer Systems Project Figure 22.3: The PIM/p parallel computer system. (Photograph from http: //www.icot.or.jp/ARCHIVE/Museum/MACHINE/pim-spec-E.html.) This basic research also led to a strong set of successful applications, in fields as diverse as theorem proving and biological computation. In a wider scope, the project was a success in terms of the research it engendered in similar international projects, such as ALVEY, ECRC, ESPRIT, INRIA, and MCC. These organizations learned from one another, and their academic competitiveness in basic research pushed them to achieve a broader range of successes. In this sense, the computer science community is very much indebted to the “fifth-generation” effort. Separately from what might or might not have been accomplished during the project, announcements about it in 1980 and 1981 provoked similar projects in the United States and in Europe. News about the project was spread by an early document titled “Preliminary Report on a Fifth Generation of Computers,” which was circulated among a few computer science researchers in the fall of 1980. Also, an international conference to announce the FGCS project was held in Tokyo in October of 1981.11 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 353
- 354. 22 The Japanese Create a Stir 22.2 Some Impacts of the Japanese Project 22.2.1 The Microelectronics and Computer Technology Corporation The announcements by MITI of plans for a fifth-generation computer system and the formation of ICOT caused alarm in the United States and Europe. The American computer industry, all too aware of growing Japanese dominance in consumer electronics and in manufacturing, worried that its current world leadership in computer technology might be eroded. William Norris, the founder of the Control Data Corporation, organized a meeting of computer industry executives in Orlando, Florida, in February 1982 to discuss the creation of a research and development consortium. Its goal would be to develop technologies that the member companies could ultimately use in their products. This meeting led in late 1982 and early 1983 to the formation of the nonprofit Microelectronics and Computer Technology Corporation (MCC) in Austin, Texas. Admiral Bobby Ray Inman, a former Director of the National Security Agency and a Deputy Director of the Central Intelligence Agency, was chosen to be its first President, Chairman, and Chief Executive Officer. Among the early joiners of the consortium were the Digital Equipment Corporation, Harris, Control Data, Sperry-Univac, RCA, NCR, Honeywell, National Semiconductor, Advanced Micro Devices, and Motorola. These were later joined by several others, including Microsoft, Boeing, GE, Lockheed, Martin Marietta, Westinghouse, 3M, Rockwell, and Kodak. The annual budget was planned to be between $50 and $100 million – depending on the number of member companies contributing funds and research personnel. At its beginning, MCC focused on four major research areas, namely, advanced computer architectures, software technology, microelectronics packaging, and computer-aided design of VLSI circuitry. AI research was to be carried out (under the eventual direction of Woodrow Bledsoe) as part of architecture research. Although the member companies did make use of some MCC-sponsored innovations, MCC itself began to decline after the departure of Inman in 1987. By that time, FGCS was perceived as less of a threat, and many of the member companies were having their own financial difficulties. Also, the Internet and the explosive growth and power of personal computers began to eclipse what was going on at MCC. The number of employees fell from its peak of about 400 in 1985 to 58 in June 2000, when the board voted to dissolve the consortium.12 354 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 355. 22.2 Some Impacts of the Japanese Project 22.2.2 The Alvey Program In March 1982, the British government set up a committee “to advise on the scope for a collaborative research programme in information technology (IT) and to make recommendations.” It was chaired by Mr. John Alvey, a senior director of British Telecommunications. In its report, issued later that year and titled “A Programme for Advanced Information Technology,” the committee noted that the Japanese FGCS project was seen “as a major competitive threat” and that anticipated responses to it by the United States “would create an equal if not greater degree of competition for the UK industry.”13 The report recommended “a five-year programme to mobilise the UK’s technical strengths in IT, through a Government-backed collaborative effort between industry, the academic sector and other research organisations. The goal [was to develop] a strong UK capability in the core enabling technologies, essential to Britain’s future competitiveness in the world IT market.” The four major technical areas identified for support were “Software Engineering, Man Machine Interfaces (MMI), Intelligent Knowledge Based Systems (IKBS) and Very Large Scale Integration (VLSI).” The recommended budget was £350 million, with the government contributing two-thirds of the cost and industry the rest. In 1983, the UK Government accepted the committee’s report and initiated the “Alvey Programme” to carry out the committee’s recommendations. A new Directorate, headed by Brian Oakley, Secretary of the Science and Engineering Research Council (SERC), was set up in the Department of Trade and Industry (DTI) to coordinate the program. Sponsorship and funds were provided by DTI, the Ministry of Defence (MoD), SERC, and industry. Among its other accomplishments, the Alvey program helped revitalize AI research in Britain. According to Oakley, “If the Lighthill Report of the early 1970s was paradise lost for the AI community, the Alvey Report of the early 1980s was paradise regained.”14 The program reached a peak level of funding of around £45 million in 1987 and went on until 1991. It is credited with energizing Britain’s computer science community by expanding research and development efforts in both academia and industry. In their excellent summary of the program, published in 1990, Brian Oakley and Kenneth Owen describe Alvey’s contributions in AI, parallel architecture, VLSI, integrated circuit CAD, software engineering, and speech technology.15 22.2.3 ESPRIT In 1983, the European Economic Community (the predecessor of the European Union) launched its ESPRIT program. (ESPRIT is an acronym for European Strategic Program of Research in Information Technology.) According to Luc Steels and Brice Lepape, who wrote an article focusing on the AI aspects of Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 355
- 356. 22 The Japanese Create a Stir ESPRIT, its goal was “to foster transnational cooperative research among industries, research organizations, and academic institutions across the European Community (EC).”16 It was also a European response to the Japanese FGCS program. ESPRIT was set up to support research in three major categories, namely, microelectronics, information processing systems (including software and advanced information processing), and applications (including computer-integrated manufacturing and office systems). Information processing, where most AI research was to be supported, was further divided into knowledge engineering, advanced architectures (including computer architectures for symbolic processing), and advanced system interfaces (speech, image, and multisensor applications). It was anticipated that the ESPRIT project would go on for ten years and would be divided into two phases, ESPRIT I and ESPRIT II. (Later, a third phase was added.) The initial budget for ESPRIT I was 1.5 billion ECUs. (The euro replaced the ECU in January 1999 at one ECU = one euro.) Funds would be provided equally between the EC and the project participants. The budget for ESPRIT II was more than double that of ESPRIT I. According to Luc Steels and Brice Lepape, by 1993, the program had “more than 6,000 scientists and engineers from about 1,500 organizations working on ESPRIT projects across EC and European Free Trade Agreement countries.” Rather than being directed in a top-down manner by program managers, the projects funded by ESPRIT resulted from proposals submitted by individual investigators and organizations. The proposals were reviewed by a distributed team of experts. The program encouraged proposals that emphasized “transnational cooperative networks,” industrial activities, and short-term gains. ESPRIT collaborated with Alvey in supporting some research in Britain.17 ESPRIT supported several AI-related projects. Among these were ones that developed various knowledge-based systems, logic programming environments, natural language parsing systems, and knowledge acquisition and machine-learning systems. As one example, I might mention the “Machine Learning Toolbox” (MLT). It was a package of machine learning techniques from which developers could select and assemble algorithms appropriate to specific kinds of tasks. Partners in its development included teams from France, the United Kingdom, Germany, Greece, and Portugal. The article by Steels and Lepape presents a thorough summary of AI efforts supported by ESPRIT.18 ESPRIT’s accomplishments helped to overcome some of industry’s reluctance about AI. While on the topic of national efforts in AI, I’ll mention the German Research Center for Artificial Intelligence (DFKI, which stands for Deutsches Forschungszentrum für Künstliche Intelligenz). It was established in 1988 and continues to conduct research in all areas of AI. More information about it can 356 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 357. 22.2 NOTES be obtained from its Web page at http://www.dfki.de/web/welcome?set language=en&cl=en. In the United States, a DARPA program analogous to Alvey and ESPRIT got underway in the early 1980s. It was partially a response to the Japanese FGCS project, but it also owed much to the observation that the time was ripe for a major program that would take advantage of ongoing technical developments in communications technology and in computer hardware and software. I’ll describe the DARPA program in the next chapter. Notes 1. “Research Report on Fifth Generation Computer Systems Project,” ICOT Progress Report, March 1983. [349] 2. Ibid. [350] 3. Shunichi Uchida, “FGCS Project: Knowledge Information Processing by Highly Parallel Processing,” Institute for New Generation Computer Technology (ICOT), Tokyo, Japan, undated. Available online at http://www.icot.or.jp/ARCHIVE/PICS/OHP/Uchi1-FGohpE.pdf. [350] 4. ICOT Staff (eds.) Proceedings of the International Conference on Fifth Generation Computer Systems, June 1–5, 1992, Tokyo, Japan: IOS Press, 1992; Institute for New Generation Computer Technology (ICOT, ed.), Proceedings of the International Conference on Fifth Generation Computer Systems, November 28–December 2, 1988, 3 volumes, Tokyo, Japan: OHMSHA, Ltd., and Berlin: Springer-Verlag, 1988; Institute for New Generation Computer Technology (ICOT, ed.), Proceedings of the International Conference on Fifth Generation Computer Systems, November 6–9, 1984, Tokyo, Japan: OHMSHA, Ltd., and Amsterdam: North-Holland, 1984. [351] 5. For a Web page describing the various PIMs, see http://www.icot.or.jp/ARCHIVE/Museum/MACHINE/pim-spec-E.html. [351] 6. See, for example, Ryuzo Hasegawa, Miyuki Koshimura, and Hiroshi Fujita, “MGTP: A Parallel Theorem Prover Based on Lazy Model Generation,” Automated Deduction – CADE-11, Lecture Notes in Computer Science, Proceedings of the 11th International Conference on Automated Deduction, Berlin/Heidelberg: Springer-Verlag, 1992. [351] 7. See, for example, Shinichi Honiden, Akihiko Ohsuga, and Naoshi Uchihira, “MENDELS ZONE: A Parallel Program Development System Based on Formal Specifications,” Information and Software Technology, Vol. 38, No. 3, pp. 181–189, March 1996. [351] 8. See, for example, Katsumi Nitta et al., “HELIC-II: Legal Reasoning System on the Parallel Inference Machine,” New Generation Computing, Vol. 11, Nos. 3–4, pp. 423–448, July 1993. [351] 9. Kazuhiro Fuchi et al., “Launching the New Era,” Communications of the ACM, Vol. 36, No. 3, pp. 49–100, March 1993. [352] 10. Ibid, p. 99. [352] 11. See, for example, T. Motooka et al., “Challenge for Knowledge Information Processing Systems (Preliminary Report on FGCS),” Proceedings of the International Conference on FGCS, JIPDEC, pp. 1–85, 1981. [353] 12. For a history of the first ten years or so of MCC, see David V. Gibson and Everett M. Rogers, R & D Collaboration on Trial: The Microelectronics and Computer Technology Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 357
- 358. 22 NOTES Corporation, Cambridge, MA: Harvard Business School Press, 1994. [354] 13. The committee’s report, from which these quotations are taken, is available online from pointers at http://www.chilton-computing.org.uk/inf/literature/reports/alvey report/p001.htm. [355] 14. Brian W. Oakley, “Intelligent Knowledge-Based Systems – AI in the U.K.,” in Ray Kurzweil, The Age of Intelligent Machines, Cambridge, MA: MIT Press, 1990. Available online at http://www.kurzweilai.net/articles/art0308.html?printable=1. [355] 15. Brian Oakley and Kenneth Owen, Alvey: Britain’s Strategic Computing Initiative, Cambridge, MA: MIT Press, 1990. [355] 16. Luc Steels and Brice Lepape, “Knowledge Engineering in ESPRIT,” IEEE Expert, Vol. 8, No. 4, pp. 4–10, August 1993. [356] 17. At this writing, there are still abundant Web pages about ESPRIT. They are available from http://cordis.europa.eu/esprit/home.html. [356] 18. Luc Steels and Brice Lepape, op. cit. [356] 358 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 359. 23.1 Chapter 23 DARPA’s Strategic Computing Program 23.1 The Strategic Computing Plan By the early 1980s expert systems and other AI technologies, such as image and speech understanding and natural language processing, were showing great promise. Also, there was dramatic progress in communications technology, computer networks and architectures, and computer storage and processing technologies. Robert Kahn (1938– ; Fig. 23.1), who had become Director of DARPA’s Information Processing Techniques Office (IPTO) in 1979, began thinking that DARPA should sponsor a major research and development program that would integrate efforts in all of these areas to create much more powerful computer systems. At the same time, there was concern that the Japanese FGCS program could threaten U.S. leadership in computer technology. With these factors as background, Kahn began planning what would come to be called the “Strategic Computing” (SC) program. Kahn had been a professor at MIT and an engineer at BBN before he joined DARPA’s IPTO as a program manager in late 1972. There he initiated and ran DARPA’s internetting program, linking the Arpanet along with the Packet Radio and Packet Satellite Nets to form the first version of today’s Internet. He and Vinton Cerf, then at Stanford, collaborated on the development of what was to become the basic architecture of the Internet and its “Transmission Control Protocol” (TCP). (TCP was later modularized and became TCP/IP, with IP standing for Internet Protocol.) Cerf joined DARPA in 1976 and led the internetting program until 1982. For their work, Kahn and Cerf shared the 2004 Turing Award of the Association for Computer Machinery.1 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 359
- 360. 23 DARPA’s Strategic Computing Program Figure 23.1: Robert E. Kahn. (Photograph courtesy of Robert E. Kahn.) Kahn thought that AI, especially expert systems, could play a major role in SC. Recall that in the mid-1970s DARPA support for AI research suffered during George Heilmeier’s tenure as the DARPA Director. A major casualty was the speech understanding program. The SC program could revitalize AI research, but more importantly in Kahn’s view, it would help transfer promising AI techniques out of university laboratories and into actual applications. Alex Roland, who wrote a well-researched book about the history of the SC program, put it this way:2 Robert Kahn and the architects of SC believed in 1983 [after the expert systems boom] that AI was ripe for exploitation. It was finally moving out of the laboratory and into the real world. . . AI would become an essential component of SC; expert systems would be the centerpiece. [They] would allow machines to “think.” Kahn saw the SC program as a pyramid of related technologies to be developed. At the base were enabling technologies such as facilities for rapid 360 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 361. 23.1 The Strategic Computing Plan design and implementation of the needed hardware. Above that sat hardware and software technologies, with AI being prominent. These would all come together in specific military systems, such as robot vehicles and aids for battle management. One of the (many) versions of this pyramid is shown in Fig. 23.2. Figure 23.2: The SC program structure and goals. (Illustration used with per- mission of DARPA.) The SC program would support, coordinate, and manage research and development for all of the technologies in the pyramid. It was to become a billion-dollar program – the largest computer research and development program ever undertaken by the U.S. government up to that time. Kahn’s boss was Robert Cooper, who became the DARPA Director in July 1981. Cooper was enthusiastic about Kahn’s ideas for the SC program, although he differed from Kahn about research strategy and how to describe the program. As Alex Roland wrote3 Kahn said build the technology base and nice things will happen. Cooper said focus on a pilot’s associate and the technology base will follow. One paradigm is technology push, the other technology pull. One is bubble up, the other trickle down. . . . The tension between them stressed SC through much of its history. Because of its size, the SC program would have to be “sold” to those Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 361
- 362. 23 DARPA’s Strategic Computing Program Congressional committees overseeing DARPA’s budget. Cooper knew that emphasizing (indeed, promising) specific military applications was how to sell Congress. He was right as far as convincing Congress was concerned, but in the end technology pull didn’t work so well. One factor in helping to convince Congress about the need for the SC program was the Japanese FGCS program. According to Roland “Congress was more exercised by Japan’s Fifth Generation program than either the Reagan administration or the computer community [including Kahn and Cooper].”4 The publication of a book5 about the Japanese project by Edward Feigenbaum and Pamela McCorduck had the effect of strengthening these concerns. In the preface to their book, they asked “Will we rise to [this crucial challenge]? If not, we may consign our nation to the role of the first great postindustrial agrarian society.” They further warned that6 . . . our national self-interest, not to mention our economic security, does not allow us [to ignore the Japanese project]. Information processing is an $88-billion-per-year industry in the United States, and its loss would be disastrous. The default of this American industry, which has led the world for decades, would be a mortal economic wound. . . . The superior technology usually wins the war – whether that war is martial, entrepreneurial, or cultural. In June 1983, Feigenbaum testified before the House Committee on Science, Space, and Technology. According to Roland he told the committee “the era of reasoning machines is inevitable. . . It is the manifest destiny of computing.”7 Kahn was persuaded to yield to Cooper’s vision about how to frame the plan, and it was finally written up in October 1983.8 Funds to support SC were approved at a level of $50 million for work to begin in fiscal year 1984. (One Congressional staff person even recommended that DARPA spend “a substantially higher amount.”) During the decade from 1983 to 1993 DARPA spent just over $1 billion on SC.9 The plan envisioned supporting two main thrusts, namely, major projects that would build specific applications and basic research to develop the “technology base” that would be needed for those applications. I will describe aspects of each of these in the following sections. 23.2 Major Projects At the beginning of the program, three major applications were chosen. To get all three of the military services to “buy in” to the program, the plan called for a “Pilot’s Associate” (for the Air Force), an aircraft carrier “Battle Management System” (for the Navy), and an “Autonomous Land Vehicle” (for the Army). However, to encourage joint service support of all of these, the plan cautioned that10 362 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 363. 23.2 Major Projects [i]t might, for example, prove preferable to pursue an autonomous underwater vehicle rather than a land vehicle, and a battle management system for land combat might prove more appropriate than that for the Naval application. As Mark Stefik wrote in his excellent review and assessment of the program, “. . . DARPA is telling the services that the particulars of these applications can be shuffled at any time, so they had better buy the whole plan.”11 Indeed, additional applications, such as an “Autonomous Air Vehicle” for launching smart bombs behind enemy lines, were undertaken later. 23.2.1 The Pilot’s Associate The Pilot’s Associate (PA) program, begun in February 1986, had as its goal the development of an interactive computer system that would aid a combat aircraft commander. Interaction with the system would be through a graphics user interface, voice recognition (capable of working under noisy and stressful conditions), and speech synthesis. It would prepare and revise mission plans, advise the pilot about targets, assess threats, help the pilot to take evasive action against interceptor missiles (flying the plane automatically in case it needed to maneuver so rapidly that the pilot might black out), and take over routine tasks. In addition to pilot inputs, the system would obtain information from navigational aids and several sensors. Advice and decisions would be based on several collaborating expert systems, automatic planning systems, and plan-execution systems. Among the technical capabilities that the 1983 DARPA strategic plan predicted could be achieved by 1989 were 10,000-rule, real-time expert systems, animated displays with 108 polygons per second, 200-word, speaker-independent speech recognition in high-noise environments, and a speech output system capable of a 1,000-word vocabulary.12 Of course, compact and aircraft-worthy hardware would be required also. The program was administered by the Avionics Laboratory at Wright-Patterson Air Force Base in Dayton, Ohio (later part of the Air Force Research Laboratory).13 After a preliminary exploratory effort (in which five contractors participated), teams led by Lockheed Aeronautical Systems (later part of Lockheed Martin Corporation) and McDonnell Douglas (later part of Boeing) were awarded contracts for work extending from 1986 through the middle of 1992. As had become standard for DARPA-managed projects, working demonstrations had to be given. According to a set of Web sites (last updated in 2004) describing the project, the Lockheed program was awarded the American Institute of Aeronautics and Astronautics (AIAA) Digital Electronics Award “. . . in recognition of outstanding achievement in. . . advancing the state-of-the-art of artificial intelligence and decision support systems into the complex, rapidly changing world of air combat.”14 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 363
- 364. 23 DARPA’s Strategic Computing Program The same site also claimed the following: Technically, by 1991, the PA was the most advanced, working, real-time intelligent system of its day and remains unsurpassed in the world. We [at Lockheed] successfully integrated 6 expert systems operating in real time in a realistic (some would say too realistic) combat simulator. The knowledge implemented in each component of this system was realistic, combat experience that was demonstrably applicable to the operation of combat aircraft today. In spite of the successful demonstrations, the Defense Department did not follow up directly in installing the technology in fighter aircraft. As one of the Web sites just cited puts it, “. . . it was left to the PA contractors to take the technology to the marketplace.”15 One of the legacies of the Pilot’s Associate program was a system that was developed for use in Army helicopters, the Rotorcraft Pilot’s Associate. A possible application involving air traffic management never materialized even though “results [of NASA studies] were enough to show that PA technology could enable free flight throughout the continental and trans-oceanic air space, with enormous savings in operational costs for the airlines and the Air Traffic Control infrastructure, while also offering an extra level of safety.”16 23.2.2 Battle Management Systems In 1984 DARPA began funding the Fleet Command Center Battle Management Program (FCCBMP, pronounced “fik bump”). A company called Analytics was the engineering contractor for FCCBMP. It provided program management support, testing, and configuration management. In a 1990 article describing and assessing the program, Rin Saunders, an engineer at Analytics, wrote that its goal was to produce a system that would “. . . assist the commander-in-chief of the U.S. Pacific Fleet (CINCPACFLT) in planning and monitoring the operation of nearly 300 ships in the Pacific and Indian ocean regions.”17 Saunders claimed that it was the most successful of the Strategic Computing programs in bringing expert systems into operational use and had “the greatest visibility and participation within the user community.” Expert systems were planned to play a major role in FCCBMP. The DARPA strategic plan envisioned ones that could process 10,000 rules per second in highly complex contexts operating at five times real time.18 FCCBMP consisted of two major expert systems communicating over a local area network. One of these, the “Force Requirements Expert System (FRESH),” was designed to keep track of the current positions and the readiness status of ships in the fleet and to issue alerts when there were significant changes. FRESH, developed by Texas Instruments, was supposed to be able to make suggestions about what should be done, such as proceeding 364 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 365. 23.2 Major Projects with the current plan anyway, expediting repairs, or substituting other ships for unready ones. Saunders claimed that19 FRESH currently provides daily alert summaries to twelve CINCPACFLT staff codes. FRESH was shown to replicate expert judgement in a trial in which FRESH and CINCPACFLT staff were given the same nomination task to solve. CINCPACFLT staff estimate that FRESH can accomplish in one minute monitoring tasks which previous[ly] took two hours, yielding a 120-fold time savings. For the planning task of nominating a replacement for a disabled ship, the time savings is 400 to 1. Another expert system, the “Capabilities Assessment Expert System” (CASES), used information about U.S. and enemy forces to provide estimates of how each would fare in hypothetical engagements. It was developed by BBN. According to Saunders,20 CASES has been used to evaluate carrier battle force operating areas; assess attack submarine employment strategies; estimate the effect of pre-D-day surveillance and early assignment of SSNs to enemy submarine attrition; and provide insights on the costs and benefits of different strike strategies relative to changes in estimated enemy capability and weather. Both expert systems made extensive use of natural language understanding and generation abilities. They were hosted on Symbolics Lisp machines and written using commercial expert-system “shells.” The battle simulations in CASES were run on an Encore parallel processor. The final prototypes for FRESH and CASES were delivered to CINCPACFLT in 1990. But when the prototype phase was complete and DARPA funding ended, the Navy decided not to continue these systems. Saunders, now a Technical Director at Computer Sciences Corporation, has provided me with some recollections about FCCBMP. These contrast a bit with what he wrote in 1990. In e-mail notes, he wrote me that21 [t]he goals of FCCBMP were an overreach for the state of the art in the 1980s. . . . [The] Navy’s decision to mothball FRESH was because there was no compelling reason to keep it. It duplicated the expert judgement of Fleet planners, in a matter of hours rather than days. But the planners were not looking to retire, and in Naval warfare, days are good enough. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 365
- 366. 23 DARPA’s Strategic Computing Program Concerning CASES, the Fleet was eager to have the simulation tools. But the goal of orchestrating the simulations in an intelligent way to evaluate what-if scenarios never got off the ground. . . . I believe that there was a growing recognition within DARPA/ISTO that the FRESH and CASES technologies’ research agendas were not best met within the vicissitudes of an operational environment. . . . There was also pressure from Congress for DARPA to divert the funding to anti-submarine warfare, which was a hot topic at the time. And relations between DARPA and Navy were always strained near the breaking point. Navy constantly fought DARPA for control of the program, both for its own sake and to redefine the program to provide greater near-term payback. In the end, everyone decided it was time to pack up and go home. 23.2.3 Autonomous Vehicles The third major applications project funded under the umbrella of DARPA’s SC program was the “Autonomous Land Vehicle” (ALV) project begun in August 1984. Martin Marietta (later to merge with Lockheed to become Lockheed Martin) was selected as the “project integrator” and funded at $10.6 million for a period of forty-two months.22 SRI, Carnegie Mellon University, the University of Maryland, Hughes Research Laboratories, Advanced Decision Systems, and the Environmental Research Institute of Michigan (ERIM) provided components and research help. The U.S. Army Engineering Topographic Laboratory helped to coordinate the work. The goals of the project were in line with the Army’s long-range “strategic vision” of using autonomous vehicles in logistics and supply operations, in search and rescue, and even in combat. A great deal of information about the ALV project can be obtained from an article by Douglas W. Gage. Rather than paraphrase his summary, I’ll quote it directly:23 The ALV was built on a Standard Manufacturing eightwheel hydrostatically-driven all-terrain vehicle capable of speeds of up to 45 mph on the highway and up to 18 mph on rough terrain. The ALV could carry six full racks of electronic equipment in dust-free air conditioned comfort, providing power from its 12-kW diesel APU. The initial sensor suite consisted of a color video camera and a laser scanner from the Environmental Research Institute of Michigan (ERIM) that returned a 64 by 256 pixel range image at 1–2 second intervals. Video and range data processing modules produced road-edge information that was used to generate a model 366 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 367. 23.2 Major Projects of the scene ahead. Higher level reasoning was performed by goalseeker and navigator modules, which then passed the desired path to the pilot module that actually steered the vehicle. A photograph of the ALV and its system configuration is shown in Fig. 23.3. Figure 23.3: Martin Marietta’s ALV (top) and its system configuration (bot- tom). (ALV photograph courtesy of DARPA; diagram from R. Terry Dunlay, “Obstacle Avoidance Perception Processing for the Autonomous Land Vehicle,” Proceedings of the IEEE Robotics and Automation Conference, pp. 912–917, Los Alamitos, CA: CS Press, 1988.) The ALV was to be the forerunner of military vehicles that could move, unguided, on roads and over rough terrain using computer vision programs to Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 367
- 368. 23 DARPA’s Strategic Computing Program inform them about their environments and planning programs and expert systems to control their routes. They would have to avoid such hazards as other vehicles, rocks, trees, ditches, water obstacles, and steep or muddy terrain. They would also have to be able to identify landmarks and other significant objects in their immediate surroundings. They were to be entirely self-contained with all computing to be done on board. The DARPA Strategic Computing Plan laid out some specific milestones for the ALV. 24 In 1985 there was to be a “road-following demonstration” in which the ALV was to navigate a preset route of 20 km at speeds up to 10 km/hour. By 1986 there was to be an “obstacle avoidance demonstration” using “fixed, polyhedral objects spaced no less than 100 m” apart and of a size much smaller than the road width. In 1990 and 1991 there was to be a “mixed road and open terrain demonstration” with speeds up to 90 km/hour on roads with other vehicles. According to a report by the National Research Council assessing progress in unmanned vehicles, “the ALV made a 1 km traverse in 1985 at an average speed of 3 km/h. . . This increased to 10 km/h over a 4 km traverse in 1986. In 1987, the ALV reached a top speed of 20 km/h. . . and used the laser scanner to avoid obstacles placed on the road.”25 The same report continues with In August 1987, the ALV performed the first autonomous cross-country traverse based on sensor data. During this and subsequent trials extending for about a year, the ALV navigated around various kinds of isolated positive obstacles over traverses of several kilometers. The terrain had steep slopes (some over 15 degrees), ravines and gulleys, large scrub oaks, brushes, and rock outcrops. Some manmade obstacles were emplaced for experiments. The smallest obstacles that could be reliably detected were on the order of 2 feet to 3 feet in height. On occasion, the vehicle would approach and detect team members and maneuver to avoid them. The vehicle reached speeds of 3.5 to 5 km/hr and completed about 75 percent of the traverses. Vision for the ALV represented the most difficult challenge. Recognizing rocks, trees, road, and ditches would stretch the state of the art of both computer vision algorithms and computer processing speeds in the 1980s. According to Roland, “DARPA estimated that the vision system for the ALV would require 10–100 billion instructions per second, compared to the rate in 1983 of only 30–40 million.”26 It has been estimated that perception for unmanned vehicles around the time of the ALV accounts for about 85% of the total computational load.27 Commenting on the performance of the ALV vision system, Roland wrote28 The vision system proved highly sensitive to environmental 368 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 369. 23.3 AI Technology Base conditions – the quality of light, the location of the sun, shadows, and so on. The system worked differently from month to month, day to day, and even test to test. Sometimes it could accurately locate the edge of the road, sometimes not. The system reliably distinguished the pavement of the road from the dirt on the shoulders, but it was fooled by dirt that was tracked onto the roadway by heavy vehicles maneuvering around the ALV. In the fall, the sun, now lower in the sky, reflected brilliantly off the myriads of polished pebbles in the tarmac itself, producing glittering reflections that confused the vehicle. Shadows from trees presented problems, as did asphalt patches from the frequent road repairs made necessary by the harsh Colorado weather and the constant pounding of the eight-ton vehicle. DARPA cancelled the ALV program in April 1988, but interest in autonomous vehicles continued in research labs, in industry, and in other government agencies. Among the programs supported by the Defense Department during the 1990s were DEMO-I for tele-operated vehicles and DEMO-II for autonomous vehicles. Again, Martin Marietta’s Aerospace Division was chosen as the overall integration contractor. Among the co-contractors providing subsystems were CMU, Hughes Research Laboratories, Advanced Decision Systems, SRI, Teleos, JPL, and the Universities of Massachusetts and Michigan. The vehicle chosen for the demonstrations was a HMMWV (an acronym for High-Mobility Multipurpose Wheeled Vehicle, pronounced humvee). A series of demonstrations was given in the mid-1990s.29 Carnegie Mellon University developed a number of important unmanned vehicles including the “Terragator” and a series of “Navlabs.” (I’ll have more to say about the Navlabs later.) Although the ALV program might not have achieved all of its goals, it can be said to have launched the era of autonomous vehicles. They are becoming more versatile and “autonomous” with each passing year. 23.3 AI Technology Base Even though the main focus of the SC program was the suite of applications just mentioned, successful pursuit of these applications required advances in the technology on which they depended. According to Robert Cooper’s view of SC, these applications would “pull” the technology of vision, robotics, expert systems, speech recognition, and natural language processing. So, in addition to the main contracts for applications, several were let for technology development. Of course, the technology developers were supposed to be closely coupled to the applications that were pulling them. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 369
- 370. 23 DARPA’s Strategic Computing Program 23.3.1 Computer Vision Ron Ohlander and (later) Robert Simpson Jr. were program managers at DARPA IPTO in charge of AI research during the SC days. Continuing the work begun earlier in “image understanding,” DARPA focused on four main areas of computer vision, namely, visual modeling and recognition, dynamic scene and motion analysis, vision-based obstacle avoidance and path planning, and implementation of vision algorithms using parallel computing architectures.30 Chuck Thorpe, Takeo Kanade, and others at CMU concentrated especially on vision systems needed by ALVs.31 Other important computer vision research in support of Strategic Computing was done at the University of Maryland under Azriel Rosenfeld, at the University of Massachusetts under Alan Hanson and Edward Riseman, at SRI under Martin Fischler, and at industrial laboratories. Besides applications in robotics, computer vision technology finds applications in cartography and photo interpretation. One of the early photo-reconnaissance systems was SCORPIUS (an acronym for Strategic Computing Object-Directed Reconnaissance Parallel-Processing Image-Understanding System) funded by the CIA and developed at the Hughes Research Labs. It was supposed to screen aerial and satellite photographs to detect ships, buildings, airplanes, and other objects of interest. According to SRI’s Martin Fischler, the project got bogged down in infrastructure problems associated with having to use a new parallel-processing computer (the “Butterfly Multiprocessor”) being developed at BBN.32 Later photo-reconnaissance projects, following on after the SC program, were more successful. One such project was RADIUS (an acronym for Research And Development for Image Understanding Systems) funded jointly by DARPA and the CIA. Image processing systems developed at SRI under the RADIUS project could overlay terrain texture on three-dimensional models of parts of the earth (enabling, for example, a simulated “flythrough” of Yosemite Valley). They could also be used to help locate buildings and other objects in photographs, thus aiding a human photo interpreter.33 23.3.2 Speech Recognition and Natural Language Processing The Pilot’s Associate project and the Battle Management projects depended on the ability of computer systems to understand verbal requests or commands. Speech recognition was needed by the Pilot’s Associate, and both speech recognition and text understanding were needed by the Battle Management projects. Accordingly, several basic research projects were funded by DARPA to advance those technologies. Recall that DARPA discontinued 370 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 371. 23.3 AI Technology Base its speech understanding research program in 1976, but continuing progress in the field, notably at CMU, MIT, and IBM, justified its resumption. Texas Instruments integrated work from eight speech recognition research projects for the Pilot’s Associate project. CMU integrated work from nine projects for the Battle Management projects. According to Roland, by the end of the SC program, speech recognition systems “could recognize 10,000 words of natural language spoken by anyone in an environment of moderate background noise and low stress on the speaker.”34 Later, at CMU, Kai-Fu Lee and others developed the speaker-independent SPHINX speech recognition system under DARPA support.35 SPHINX used HMMs (hidden Markov models) and statistical information about the likelihoods of word strings to aid recognition. Speech recognition work continues at several industrial and university research laboratories. At CMU, to name just one example, the SPHINX project has assembled “a set of reasonably mature [open source], world-class speech components that provide a basic level of technology to anyone interested in creating speech-using applications.” However, the project Web page at SourceForge36 cautions that “. . . SPHINX is not a final product. Those with a certain level of expertise can achieve great results with the versions of SPHINX available here, but a naive user will certainly need further help. In other words, the software available here is not meant for users with no experience in speech, but for expert users.” Advances in computer speed and memory have led to several high-quality, real-time, and commercially available speech recognition systems of moderate cost. The SC program also sponsored basic research work on text understanding as part of what DARPA called “new generation systems.” Seven contractors received DARPA awards in 1984.37 BBN Laboratories, USC/Information Sciences Institute, the University of Pennsylvania, and the University of Massachusetts worked on natural language interfaces that could respond to typed queries. New York University, the Systems Development Corporation (later to become part of Unisys), and SRI International worked on understanding free-form text from military messages. BBN and USC–ISI coordinated the work on interfaces based initially on BBN’s IRUS system, which later grew into JANUS.38 New York University and SDC coordinated the work on text understanding and developed the PROTEUS and PUNDIT systems.39 According to Roland, the results of this work “were most promising, again exceeding the milestones laid out in the original SC plan. . . . the metrics of the original plan [were] exceeded in error rate and [in] the size of vocabularies.”40 Roland goes on to elaborate:41 . . . BBN’s IRUS system, with a vocabulary of 4,500 words and 1,200 domain concepts, received a favorable response in 1986 when it was installed in a Battle Management Program test bed. By 1987 BBN Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 371
- 372. 23 DARPA’s Strategic Computing Program IRUS was becoming JANUS, a system designed to understand vague and confusing queries. JANUS was successfully demonstrated in the Air-Land Battle Management Program in the fall of 1987. Meanwhile in 1986 the first version of the PROTEUS system successfully processed naval casualty reports (CASREPS) about equipment failure. 23.3.3 Expert Systems Because expert systems appeared to be so promising in the late 1970s and early 1980s, they were slated for a prominent role in the Strategic Computing program. They were to be the reasoning agents that would give “intelligence” to the SC applications. Ronald Ohlander, the DARPA program manager for intelligent systems, sought contractors for expert systems research in 1984. Out of the fifty proposals that were submitted to DARPA, Ohlander recommended that six of them be funded. A seventh was eventually added to this list. As Roland reports, Stanford would work on new expert system architectures, BBN would work on the problem of getting the needed knowledge into expert systems, Ohio State University would work on methods for expert systems to explain their conclusions, and the University of Massachusetts and General Electric would work on techniques for reasoning with uncertain information.42 The other contractors were start-up companies – both founded by Edward Feigenbaum and other Stanford researchers. One, IntelliCorp (originally founded as IntelliGenetics), had already built an EMYCIN-inspired expert system shell called KEE (an acronym for Knowledge Engineering Environment).43 IntelliCorp proposed to extend KEE by upgrading several features, including the ability to maintain the consistency of the knowledge base as new knowledge is added or deleted, the ability to make reasonable inferences even though they might not be warranted by specific information in its knowledge base, and the ability to make plans and schedules. It would also have facilities for knowledge acquisition, user interface construction, and hierarchical descriptions of objects. IntelliCorp was awarded a DARPA contract in 1984 for $1,286,781.44 KEE eventually evolved into a system called OPUS.45 The other start-up company, Teknowledge, Inc., proposed to build an expert-system toolkit called ABE to be used for building expert systems. ABE was to be brand new, and thus it was somewhat of a gamble for DARPA, but the company had plenty of AI experts either on its payroll or as consultants.46 Teknowledge was awarded a DARPA contract for $1,813,260 to make good on its promises.47 Both KEE and ABE were used by the SC applications contractors, but neither provided the intelligence originally expected from expert systems. But these expectations were probably unrealistic. In their 1994 assessment of 372 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 373. 23.4 Assessment knowledge-based systems (KBSs), Frederick Hayes-Roth and Neil Jacobstein, two pioneers in expert system applications, wrote “. . . although the current generation of expert and KBS technologies had no hope of producing a robust and general human-like intelligence, many people were disappointed that it did not.”48 Even so, Hayes-Roth and Jacobstein concluded the following:49 KBS have been remarkably effective over the past decade of industrial experience – often delivering order-of-magnitude increases in speed, quality, or cost performance. They have penetrated every major institution from Fortune 500 companies to small entrepreneurial firms, military services, government agencies, health care, and educational institutions. KBS applications may now be found in virtually every field of human endeavor from music to medicine. DART, an acronym for Dynamic Analysis and Replanning Tool, was a KBS whose roots can be traced to AI research during the SC program. Developed at BBN, it helped plan the movement of equipment and personnel from Europe to Saudi Arabia during the 1990 Persian Gulf War. In fact, Victor Reis, the Director of DARPA at the time, has been quoted as claiming “The DART scheduling application paid back all of DARPAs 30 years of investment in AI in a matter of a few months.”50 23.4 Assessment What can be said about the SC program overall? It has been criticized both by those who thought it might achieve its military goals and also by those who faulted it for not having done so. Among the former, the Computer Professionals for Social Responsibility (CPSR) had several concerns, stated in one of their newsletters:51 1. The SCI [Strategic Computing Initiative] promises specific new weapons systems; autonomous vehicles, such as robot tanks; a combat pilot’s “associate;” and an aircraft carrier group “battle management system.” Our concern is that proposals for computer research will be assessed by their relevance to these specific applications, rather than by their general scientific merit. 2. The SCI promotes the use of machine “intelligence,” to control the operation of complex military systems under unpredictable circumstances. Our concern is that, particularly when the stakes are high, situations of extreme uncertainty are precisely the wrong environment for the application of artificial intelligence. 3. The SCI promotes the military application of computer technology as a solution to perceived problems in defense. Our Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 373
- 374. 23 DARPA’s Strategic Computing Program concern is that, rather than increasing our security, past attempts to achieve superiority in new weapons technology have fueled an arms race that has no foreseeable end. This last concern is the gravest. In the final analysis, we believe neither that the path to national security lies in military superiority, nor that superiority can be achieved through the use of computers. Most commentators doubt that the SC program achieved its goal of pulling new AI technology into the SC applications. Developing the kinds of capabilities envisioned by the SC applications required AI inventions, and the atmosphere needed for invention is not conducive to tightly programmed milestone demonstrations. Instead, as Roland comments, the “applications extemporized ad-hoc, off-the-shelf solutions to meet demonstration deadlines.”52 Furthermore, the showcase systems, namely, the ALVs, the Pilot’s Associate, and those for battle management, were not immediately “bought” by their hoped-for military customers. Even so, these systems were the forerunners of similar ones having much higher levels of performance. Generous SC support for the AI “technology base” nourished AI research in general even though the research did not produce results that were integrated into the SC applications. After a short diminution toward the end of the SC program, AI research has steadily prospered both at universities and in industry and continues to produce important new capabilities. In addition to the problem of combining invention with application, the SC program suffered from institutional problems and budget reductions. There was often friction between the DARPA people and the military customers. Furthermore, many of the people who had planned the SC program, including Kahn and Cooper, had left DARPA by late 1985. In the spring of 1986, DARPA combined its Information Processing Techniques Office with its Engineering Applications Office and renamed it the Information Systems Technology Office (ISTO). A succession of ISTO directors both endured and helped cause budget fluctuations. Jacob Schwartz, who was skeptical about some AI approaches, became the ISTO director in September 1987. He promptly canceled some AI programs and failed to renew others. Then, in 1991 ISTO split into SISTO (Software and Intelligent Systems Technology Office) and CSTO (Computer Systems Technology Office), effectively ending attempts to couple basic research with applications. The SC program gradually disappeared from view. According to Roland it was never mentioned in public documents or reports after 1989. It vanished from the DARPA budget in 1993 and was ultimately replaced by other programs including one for High Performance Computing (HPC).53 Even though the program itself disappeared, its accomplishments, along with those of the other “new-generation” projects, were many. Progress made during the 1980s established artificial intelligence as a technology that was capable of 374 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 375. 23.4 NOTES taking on a wide variety of real-world applications. As I’ll describe in a subsequent chapter, much new AI technology was invented during the 1980s and 1990s, and some of it at least was stimulated by the new-generation projects. However, before continuing with my roughly chronological history, I’ll make a temporary diversion to discuss controversies that were simmering on the sidelines and within the field itself. Notes 1. To learn more about the evolution of the Internet, see Robert E. Kahn and Vinton G. Cerf, “What Is the Internet (And What Makes It Work)” available online at http://www.cnri.reston.va.us/what is internet.html. [359] 2. Alex Roland (with Philip Shiman), Strategic Computing: DARPA and the Quest for Machine Intelligence, 1983–1993, pp. 191–192, Cambridge, MA: MIT Press, 2002. [360] 3. Ibid, p. 71. [361] 4. Ibid, p. 91. [362] 5. Edward Feigenbaum and Pamela McCorduck, The Fifth Generation: Japan’s Computer Challenge to the World, Boston, MA: Addison-Wesley Longman Publishing Co., Inc., 1983. An article with the same title by the same authors appeared in Creative Computing, Vol. 10, No. 8, p. 103, August 1984, and is available online at http://www.atarimagazines.com/creative/v10n8/103 The fifth generation Jap.php. [362] 6. Edward Feigenbaum and Pamela McCorduck, op. cit., pp. 19–20. [362] 7. In Alex Roland, op. cit., pp. 91–92. Roland cites U.S. Congress, House, Committee on Science, Space, and Technology, Japanese Technological Advances and Possible U.S. Responses Using Research Joint Ventures. Hearings, 98th Congress, 1st session, June, 29–30, 1983, pp. 116–143, at p. 119. [362] 8. “Strategic Computing: New-Generation Computing Technology: A Strategic Plan for Its Development and Applications to Critical Problems in Defense,” Defense Advanced Research Projects Agency, Arlington, Virginia, October 28, 1983. [362] 9. Alex Roland, op. cit., p. 319. [362] 10. “Strategic Computing: New-Generation Computing Technology: A Strategic Plan for its Development and Applications to Critical Problems in Defense,” p. 20, Defense Advanced Research Projects Agency, Arlington, Virginia, October 28, 1983.. [362] 11. Mark Stefik, “Strategic Computing at DARPA: An Overview and Assessment,” Communications of the ACM, Vol. 28, No. 7, pp. 690–704, July 1985. [363] 12. “Strategic Computing: New-Generation Computing Technology: A Strategic Plan for Its Development and Applications to Critical Problems in Defense,” Chart I.2 of appendix I, Defense Advanced Research Projects Agency, Arlington, Virginia, October 28, 1983. [363] 13. For a description and review of the program by people from Wright-Patterson Air Force Base see Sheila B. Banks and Carl S. Lizza, “Pilot’s Associate: A Cooperative, Knowledge-Based System Application,”IEEE Expert, Vol. 6, No. 3, pp. 18–29, 1991. [363] 14. http://www.dms489.com/PA/PA index.html. [363] 15. http://www.dms489.com/PA/PA index.html. [364] 16. http://www.dms489.com/PA/PA index.html. [364] 17. Rin Saunders, “The Fleet Command Center Battle Management Project: Lessons Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 375
- 376. 23 NOTES Learned,” Proceedings of the IEEE Conference on Managing Expert System Programs and Projects, pp. 51–60, September 1990. [364] 18. “Strategic Computing: New-Generation Computing Technology: A Strategic Plan for its Development and Applications to Critical Problems in Defense,” Chart I.3 of Appendix I, Defense Advanced Research Projects Agency, Arlington, Virginia, October 28, 1983. [364] 19. Rin Saunders, op. cit., p. 53. [365] 20. Ibid, p. 53. [365] 21. E-mail correspondence of December 12, 2007. [365] 22. Alex Roland, op. cit., p. 222. [366] 23. Douglas W. Gage, “UGV HISTORY 101: A Brief History of Unmanned Ground Vehicle (UGV) Development Efforts,” Unmanned Systems Magazine, Special Issue on Unmanned Ground Vehicles, Vol. 13, No. 3, Summer 1995. [366] 24. “Strategic Computing: New-Generation Computing Technology: A Strategic Plan for Its Development and Applications to Critical Problems in Defense,” Defense Advanced Research Projects Agency, Arlington, Virginia, October 28, 1983. The milestones described in this paragraph, along with many others, are taken from Chart I.1 in Appendix I. [368] 25. National Research Council Staff, Technology Development for Army Unmanned Ground Vehicles, pp. 152–153, Washington, DC: National Academies Press, 2003. [368] 26. Alex Roland, op. cit.. [368] 27. National Research Council Staff, Technology Development for Army Unmanned Ground Vehicles, p. 148, Washington, DC: National Academies Press, 2003. [368] 28. Alex Roland op. cit.. [368] 29. For summaries of DEMO-I and DEMO-II technology and demonstrations, see Douglas W. Gage, “UGV HISTORY 101: A Brief History of Unmanned Ground Vehicle (UGV) Development Efforts,” Unmanned Systems Magazine, Special Issue on Unmanned Ground Vehicles, Vol. 13, No. 3, Summer 1995, and National Research Council Staff, Technology Development for Army Unmanned Ground Vehicles, p. 148, Washington, DC: National Academies Press, 2003. The latter book recommends Oscar Firschein and Thomas Strat (eds.), Reconnaissance, Surveillance, and Target Acquisition for the Unmanned Ground Vehicle: Providing Surveillance “Eyes” for an Autonomous Vehicle, San Francisco, CA: Morgan Kaufmann Publishers, 1997. [369] 30. See, for example, Robert L. Simpson Jr., “Computer Vision: An Overview,” Guest Editor’s Introduction, IEEE Expert, pp. 11–15, August 1991. [370] 31. See, for example, Takeo Kanade, Chuck Thorpe, and William Whittaker, “Autonomous Land Vehicle Project at CMU,” Proceedings of the 1986 ACM Computer Conference, pp. 71–80, February 1986, and Yoshimasa Goto and Anthony Stentz, “Mobile Robot Navigation: The CMU System,” IEEE Expert, pp. 44–54. 1987. (The latter paper is available online at http://www.ri.cmu.edu/pub files/pub3/goto y 1987 1/goto y 1987 1.pdf.) [370] 32. Personal communication, November 15, 2007. [370] 33. See Thomas M. Strat and Oscar Firschein, RADIUS: Image Understanding for Imagery Intelligence, San Francisco: Morgan Kaufmann Publishers, 1997. See also the RADIUS Web site at http://www.ai.sri.com/∼radius/. [370] 34. Alex Roland, op. cit. For this information, Roland cites Victor Zue, a speech researcher and professor at MIT. [371] 35. Kai-Fu Lee, Hsiao-Wuen Hon, and Raj Reddy, “An Overview of the SPHINX Speech Recognition System,” IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 38, No. 1, January 1990. Available online at http://www.ri.cmu.edu/pub files/pub2/lee k f 1990 1/lee k f 1990 1.pdf. [371] 376 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 377. 23.4 NOTES 36. http://cmusphinx.sourceforge.net/html/cmusphinx.php. [371] 37. Proceedings of the Workshop on Strategic Computing Natural Language, Foreword by Robert Simpson, Morristown, NJ: Association for Computational Linguistics, 1986. [371] 38. For a brief history of the IRUS/JANUS work (as well as related work at BBN), see Ralph Weischedel, “Natural-Language Understanding at BBN,” IEEE Annals of the History of Computing, pp. 46–55, January–March 2006. A BBN report about the project is available online a http://www.aclweb.org/anthology-new/H/H86/H86-1001.pdf. [371] 39. See the online document by Ralph Grishman and Lynette Hirschman, “PROTEUS and PUNDIT: Research in Text Understanding,” available at http://www.aclweb.org/anthology-new/H/H86/H86-1002.pdf. [371] 40. Alex Roland, op. cit., p. 212. [371] 41. Ibid, pp. 269–270. [371] 42. Ibid, p. 195. [372] 43. T. P. Kehler and G. D. Clemenson, “KEE – The Knowledge Engineering Environment for Industry,” Systems and Software, Vol. 3, No. 1, pp. 212–224, January 1984. [372] 44. Alex Roland, op. cit. p. 198. [372] 45. Richard Fikes et al., “OPUS: A New Generation Knowledge Engineering Environment,” Phase 1 Final Report, lntelliCorp, Mountain View, CA, 1987. [372] 46. Lee D. Erman, Jay S. Lark, and Frederick Hayes-Roth, “ABE: An Environment for Engineering Intelligent Systems,” IEEE Transactions on Software Engineering, Vol. 14, No. 12, pp. 1758–1770, December 1988. [372] 47. Alex Roland, op. cit., p. 201. [372] 48. Frederick Hayes-Roth and Neil Jacobstein, “The State of Knowledge-Based Systems,” Communications of the ACM, Vol. 37, No. 3, p. 36, March 1994. [373] 49. Ibid, p. 36. [373] 50. Sara Reese Hedberg, “DART: Revolutionizing Logistics Planning,” IEEE Intelligent Systems, p. 81, May/June 2002. [373] 51. The CPSR Newsletter, Vol. 2, No. 2, Spring 1984. Also see S. M. Ornstein, B. C. Smith, and L. A. Suchman, “Strategic Computing: An Assessment,” Bulletin of the Atomic Scientists, Vol. 40, No. 10, pp. 11–15, December 1984. [373] 52. Alex Roland, op. cit., p. 243. [374] 53. Ibid, p. 285. [374] Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 377
- 378. 23 NOTES 378 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 379. 23.4 Part VI Entr’acte Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 379
- 380. 23 380 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 381. 24.1 Chapter 24 Speed Bumps There have been naysayers from the earliest days of artificial intelligence. Alan Turing anticipated (and dealt with) some of their objections in his 1950 paper. In this chapter, I’ll recount some of the controversies surrounding AI – including some not foreseen by Turing. I’ll also describe some formidable technical difficulties confronting the field. By the mid-1980s or so, these difficulties had caused some to be rather dismissive about progress up to that time and pessimistic about the possibility of further progress. For example, in wondering about the need for a special issue of the journal Dædalus devoted to AI in 1988, the philosopher Hilary Putnam wrote1 “What’s all the fuss about now? Why a whole issue of Dædalus? Why don’t we wait until AI achieves something and then have an issue?” The attacks and expressions of disappointment from outside the field helped precipitate what some have called an “AI winter.” 24.1 Opinions from Various Onlookers 24.1.1 The Mind Is Not a Machine In the introduction to his edited volume of essays titled Minds and Machines,2 the philosopher Alan Ross Anderson mentions the following two extreme opinions regarding whether or not the mind is a machine: (1) We might say that human beings are merely very elaborate bits of clockwork, and that our having “minds” is simply a consequence of the fact that the clockwork is very elaborate, or (2) we might say that any machine is merely a product of human ingenuity (in principle nothing more than a shovel), and that Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 381
- 382. 24 Speed Bumps though we have minds, we cannot impart that peculiar feature of ours to anything except our offspring: no machine can acquire this uniquely human characteristic. Most AI researchers probably agree with the first of these two statements. I certainly do (although I would not have used the word “merely”). Marvin Minsky put this position most powerfully when he is alleged to have said “The mind is a meat machine.” However, some philosophers hold to the second view. The most prominent of these is probably the British philosopher, John R. Lucas (1929– ). In an essay titled “Minds, Machines, and Gödel,”3 Lucas based his argument on Kurt Gödel’s proof that there are some true statements that cannot be proved by any mechanical system that is both consistent and able (at least) to do arithmetic. Lucas presumes that humans (or at least some humans) can “see” these statements to be true even though machines cannot prove them. Several people have pointed to flaws in Lucas’s argument,4 and Lucas claims to have responded to at least some of them in his book The Freedom of the Will.5 In a 1990 paper read to the Turing Conference at Brighton, Lucas seems to have weakened his argument a bit by saying6 The argument I put forward is a two-level one. I do not offer a simple knock-down proof that minds are inherently better than machines, but a schema for constructing a disproof of any plausible mechanist thesis that might be proposed. . . . Essentially, therefore, the two parts of my argument are first a hard negative argument, addressed to a mechanist putting forward a particular claim, and proving to him, by means he must acknowledge to be valid, that his claim is untenable, and secondly a hand-waving positive argument, addressed to intelligent men, bystanders as well as mechanists espousing particular versions of mechanism, to the effect that some sort of argument on these lines can always be found to deal with any further version of mechanism that may be thought up. I happen to believe that humans are subject to whatever Gödelian limitations might apply to machines, but that’s because I believe humans are machines. Lucas continues to argue his point because (I think) he would like to believe they are not. In any case, the argument is somewhat sterile because it does not really limit what AI can potentially do in practice. Even Lucas admitted in his original paper that we might be capable of “constructing very, very complicated systems of, say, valves and relays,” that would be “capable of doing things which we recognized as intelligent, and not just mistakes or random shots, but which we had not programmed into it. But then it would cease to be a machine.” (Here, he seems to be trying to win his argument by redefining “machine.”) 382 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 383. 24.1 Opinions from Various Onlookers The engineer Mortimer Taube (1910–1965) also believed that humans were not machines. In his 1961 book Computers and Common Sense: The Myth of Thinking Machines,7 he railed against efforts to get computers to reason, to translate human languages, and to learn. Many of the things he said that computers would not be able to do have, by now, been done. 24.1.2 The Mind Is Not a Computer A. New Physics Is Needed Several people have put forward the argument that, although humans may well be machines, intelligence cannot be exhibited by computers – at least not by present-day computers made of transistors and other ordinary electromagnetic components and working the way they do. The British physicist Sir Roger Penrose (1931– ; Fig. 24.1) is persuaded by Lucas’s Gödelian arguments about the limitations of computers. (Penrose is famous for work in quantum physics, relativity theory, the structure of the universe, and “Penrose tilings.”) He, like Lucas, believes that computers could never be conscious, nor could they have the full range of human intelligence. But Penrose imagines that these limitations apply only to machines based on the presently known laws of physics. To escape from Gödel’s limitations (as Penrose believes brains do), he claims a new kind of physics must be invoked – one that involves something he calls “correct quantum gravity.” Unfortunately, correct quantum gravity, whatever it is, remains to be discovered (or, I would rather say, invented.) Penrose puts forward these ideas (along with some very engaging material about physics) in two books: The Emperor’s New Mind8 and Shadows of the Mind: A Search for the Missing Science of Consciousness.9 I, along with many others, am skeptical that a new physics is needed to realize all of AI’s ambitions. (But, of course, we have not realized them yet.) Penrose attempts to answer some of the criticisms of his views in his article “Beyond the Doubting of a Shadow: A Reply to Commentaries on Shadows of the Mind.”10 B. Intentionality Is Needed The American philosopher John Searle (1932– ; Fig. 24.2) argues that computational processes, as we know them, do not have something humans do have – something he and some other philosophers call “intentionality.” Intentionality has to do with attaching “meaning” to objects and to properties of objects. Searle’s definition is as follows: “Intentionality is . . . that feature of certain mental states by which they are directed at or about objects and states of affairs in the world.” For example, according to Searle, “beliefs, desires, and intentions are intentional states.”11 Thus, he would claim, although it is possible to represent the phrase “John is tall” in a computer, say as a logical Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 383
- 384. 24 Speed Bumps Figure 24.1: Sir Roger Penrose. expression such as “G33(K077),” there is no way for the computer to know that G33 refers to the “in-the-world” property of “tallness” nor that K077 refers to the actual John “in the world.” In short, computational processes lack “aboutness”; they don’t know what their symbols are about. In contrast, when humans use words, they know what those words are about. Searle is famous among AI researchers and philosophers for a thought experiment he proposed about “understanding.” It has come to be called “the Chinese Room” experiment.12 Searle sets up the thought experiment by writing Suppose that I’m locked in a room and given a large batch of Chinese writing. Suppose furthermore (as is indeed the case) that I know no Chinese, either written or spoken, and that I’m not even confident that I could recognize Chinese writing as Chinese writing distinct from, say, Japanese writing or meaningless squiggles. To make his experiment relevant to AI work about “story understanding,” 384 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 385. 24.1 Opinions from Various Onlookers Figure 24.2: John Searle. (Photograph courtesy of John Searle.) Searle imagines that his room contains two batches of Chinese symbols, which, unknown to Searle, constitute a story and general background information about such stories. The room also contains rules, written in English, about how to manipulate sets of Chinese characters and how to generate Chinese characters as a result of such manipulations. Into such a room, then, comes a third batch of Chinese symbols. As Searle puts it, he has rules in the room (written in English) that instruct me how to give back certain Chinese symbols with certain sorts of shapes in response to certain sorts of shapes given me in the third batch. Unknown to me, the people who are giving me all of these symbols call [the first two batches a story and its background information] and they call the third batch “questions.” Furthermore, they call the symbols I give them back in response to the third batch “answers to the questions,” and the set of rules in English that they gave me, they call the “program.” . . . Suppose also that after a while I get so good at following the instructions for manipulating the Chinese symbols and the programmers get so good at writing the programs that from the external point of view Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 385
- 386. 24 Speed Bumps – that is, from the point of view of somebody outside the room in which I am locked – my answers to the questions are absolutely indistinguishable from those of native Chinese speakers. Nobody just looking at my answers can tell that I don’t speak a word of Chinese. Searle’s question, essentially, is “Can it be said that the room (containing Searle, the rules, and the batches of Chinese symbols) ‘understands’ Chinese?” Searle claims the answer is “no” because all that is going on is “formal symbol manipulation” without understanding what the symbols mean. In Searle’s words: Because the formal symbol manipulations by themselves don’t have any intentionality; they are quite meaningless; they aren’t even symbol manipulations, since the symbols don’t symbolize anything. In the linguistic jargon, they have only a syntax but no semantics. Such intentionality as computers appear to have is solely in the minds of those who program them and those who use them, those who send in the input and those who interpret the output. While acknowledging that the Chinese room “simulates” understanding, he distinguishes between simulations and “the real thing.” He wrote No one supposes that computer simulations of a five-alarm fire will burn the neighborhood down or that a computer simulation of a rainstorm will leave us all drenched. Why on earth would anyone suppose that a computer simulation of understanding actually understood anything? . . . For simulation, all you need is the right input and output and a program in the middle that transforms the former into the latter. That is all the computer has for anything it does. To confuse simulation with duplication is the same mistake, whether it is pain, love, cognition, fires, or rainstorms. Searle’s Chinese Room reminds me of Herb Simon’s experiment in simulating the execution of the Logic Theorist (LT) program. Recall from Section 3.2 that LT began by hand simulation, using Simon’s children as the computing elements, while writing on and holding up note cards as the registers that contained the state variables of the program. Presumably, the children knew nothing about propositional logic, yet the whole assemblage, Simon, the children, and the note cards, proved a theorem. Apparently, “simulating” the proof of a theorem is pretty much the same as actually proving a theorem – just as simulating addition is the same as addition. Could it be that simulating understanding is really the same as real understanding? There are several possible responses to Searle’s arguments, and there is no shortage of responders! In his paper, Searle anticipates many potential replies, 386 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 387. 24.1 Opinions from Various Onlookers and twenty-eight actual replies were published along with Searle’s original paper. Here is what I think: Let’s imagine that we can look inside Searle’s brain when he is in the process of understanding a question put to him in English. There are, presumably, trillions of synapses engaged in firing and inhibiting billions of neurons in a coordinated effort to make sense of the question and to compose and deliver an answer. We would not claim that any individual synapse nor the neurons it connects is understanding English. The process of “understanding” is not a concept that is meaningful at the level of detail appropriate for analyzing the workings of neurons. Analogously, the process of proving a theorem by a computer (or by Simon’s children) is not a concept that is meaningful at the level of individual transistors (or children holding note cards). In explaining phenomena, either of the brain or of computers (or of anything else), we use concepts matched to the level of explanation. The concept of “understanding” is a concept we find useful to apply to mental activities viewed at the “whole-person level,” not at the nerve-cell level. Similarly, we would, I think, find it useful to say that the assemblage of room, Searle, rules, and Chinese characters understood Chinese. But what about meaning and intentionality? If we write G33(K077) in computer memory, does it “mean” anything? Well, it depends on what else is in computer memory – especially what else in memory is linked to the symbols G33 and K077. The symbols and the links between them constitute a network, and it is the whole network that contains the meanings. Recall the question M. Ross Quillian was attempting to answer in his 1966 dissertation, namely, “What sort of representational format can permit the ‘meanings’ of words to be stored, so that humanlike use of these meanings is possible?” Perhaps it’s worth repeating here something I wrote in Section 6.3: According to Quillian, the meaning of a term is represented by its place in the network and how it is connected to other terms. This same idea is used in dictionaries where the meaning of a word is given by mentioning the relationship of this word to other words. The meanings of those other words are, in turn, given by their relationships to yet other words. So we can think of a dictionary as being like a large semantic network of words linked to other words. In some cases, it is also necessary to link a network’s symbols to actual objects in the world through a computer’s sensory and motor facilities. Newell and Simon anticipated this need in their paper about the physical symbol system hypothesis (PSSH). That hypothesis claims that a physical symbol system (such as a computer) has the necessary and sufficient conditions for intelligent behavior. Newell and Simon wrote13 A physical symbol system is a machine that produces through time an evolving collection of symbol structures. Such a system exists in Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 387
- 388. 24 Speed Bumps a world of objects wider than just these symbolic expressions themselves. Regarding this “world of objects,” a physical symbol system includes (in addition to its means for formal symbol manipulation) the ability to “designate.” Here is Newell and Simon’s definition: “An expression [composed of symbols] designates an object if, given the expression, the system can either affect the object itself or behave in ways dependent on the object ” (my italics). So, I believe Searle is simply confused about some basic ideas in computer science. Although it has not yet been empirically established that a computer (manipulating symbols and attached as needed to its environment) can be made to exhibit all of the aspects of intelligent behavior of which humans are capable, I don’t believe that Searle’s thought experiment casts doubt on the possibility. Searle himself believes that physical systems of some sort can be intelligent and understand things. He believes that humans are one kind of such a system. He wrote as follows: “Could a machine think?” The answer is, obviously, yes. We are precisely such machines. “Yes, but could an artifact, a man-made machine think?” Assuming it is possible to produce artificially a machine with a nervous system, neurons with axons and dendrites, and all the rest of it, sufficiently like ours, again the answer to the question seems to be obviously, yes. Yet, Searle gives us no clue as to what it is about brains, composed of neurons, that is different from computers, composed of transistors, that endows the former, but not the latter, with intentionality. He claims that for a machine to think it would have to have “internal causal powers” equivalent to those of brains. He does not say just what these internal causal powers might be. C. Strong and Weak AI Searle’s paper introduced definitions for “strong AI” and “weak AI” that are useful for distinguishing between two types of AI endeavors. Strong AI is associated with the claim that an appropriately programmed computer could be a mind and could think at least as well as humans do. Achieving strong AI is the ultimate goal for many artificial intelligence researchers. Searle’s article attempts to show that strong AI (using computers) is impossible. However, practitioners of weak (or “cautious”) AI use programs as a tool to study the mind by formulating and testing hypotheses about it. Weak AI has also come 388 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 389. 24.1 Opinions from Various Onlookers to be associated with attempts to build programs that aid, rather than duplicate, human mental activities. Weak AI has already been (and continues to be) quite successful, whereas the quest for strong AI will no doubt go on for a rather long time. D. “Global Processes” Are Needed Hubert L. Dreyfus (Fig. 24.3), now a philosophy professor at UC Berkeley, began his career teaching philosophy at MIT.14 He first encountered the AI enterprise there, and in the early 1960s he and his brother, Stuart, attended a talk by Herb Simon. Several things about AI and about what they heard in the talk rankled the brothers Dreyfus. At about that time, the RAND Corporation in Santa Monica, California, thought that having a philosopher on board along with their computer people would be a good idea. Stuart, a specialist in operations research who was working at RAND, recommended Hubert. So Hubert spent the summer of 1961 at RAND as a consultant studying AI research. Shortly after the summer, Hubert wrote a RAND paper titled “Alchemy and Artificial Intelligence,” in which, among other things, he concluded that the ultimate goals of AI research were as unachievable as were those of alchemy.15 Figure 24.3: Hubert Dreyfus. (Copyright photo: Sijmen Hendriks. Used with permission from Sijmen Hendriks.) In his paper, Dreyfus evaluated AI progress in four areas, namely, game playing, problem solving (including theorem proving), language translation, and pattern recognition. He wrote Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 389
- 390. 24 Speed Bumps An overall pattern is taking shape: an early, dramatic success based on the easy performance of simple tasks, or low-quality work on complex tasks, and then diminishing returns, disenchantment, and, in some cases, pessimism. A typical case, he claimed, was Gelernter’s geometry-theorem proving machine: “No more striking example exists of an ‘astonishing’ early success and the equally astonishing failure to follow it up.” And, answering AI’s claim that progress is being made, he wrote “According to this definition [of progress], the first man to climb a tree could claim tangible progress toward flight to the moon.” One of the reasons for this stagnation, according to Dreyfus, was that AI research is based on the assumption that thinking can be analyzed as a finite set of simple determinate operations (such as the application of rules to a finite set of data). Rather, he claimed, “thinking involves global processes, which cannot be understood in terms of a sequence or even a parallel set of discrete steps.” These global processes are manifest in three ways. The first is “fringe consciousness.” It is what the brain uses to access the infinite “open-ended information characteristic of everyday experience.” Fringe consciousness allows humans to consider details and the big picture simultaneously. Another global process is at work in human thinking when we distinguish the essential from the unessential. The third is “global context,” which allows us to reduce ambiguity. A combination of these abilities permits what he calls “perspicuous grouping” – what the brain does when it recognizes complex patterns, such as human faces, for example. Dreyfus claimed that computer programs are unable to employ these global processes, which are essential for intelligent behavior. Dreyfus stated that the brain processes information in an entirely different way than a computer does. He wrote that information in the brain is “processed globally the way a resistor analogue [a kind of analog computer] solves the problem of the minimal path through a network.” Furthermore, he said that the “body plays a crucial role in making possible intelligent behavior.” Several other people have emphasized the importance of “embodiment” for progress in AI, and I’ll have more to say about that topic shortly. About the future (as judged from the early 1960s), Dreyfus wrote Only experimentation can determine the extent to which newer and faster machines, better programming languages, and clever heuristics can continue to push back the frontier. Nonetheless, the dramatic slowdown in the fields we have considered and the general failure to fulfill earlier predictions suggest the boundary may be near. Dreyfus’s comments on AI should not be taken to imply that he thought that human-level artificial intelligence by machines is impossible – he just 390 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 391. 24.1 Opinions from Various Onlookers thought (and still thinks) that it is impossible using the methods of what the philosopher John Haugeland called “good old-fashioned artificial intelligence” (GOFAI),16 namely, the kind that uses heuristic search and discrete collections of symbolically represented facts and rules. He acknowledged that, in principle, “we could simulate intelligent behavior if we could build or simulate a device which functioned exactly like the human brain.” But, he thought, such a simulation could not be realized in practice. “We do not know the equations describing the physical processes in the brain, and even if we did, the solution of the equations describing the simplest reaction would take a prohibitive amount of time.” The summary of his paper concluded with “Significant developments in artificial intelligence. . . must await an entirely different sort of computer. The only existing prototype for it is the little-understood human brain.” The main ideas of his RAND paper have been presented and expanded in several of Dreyfus’s books and articles.17 Pamela McCorduck’s book, Machines Who Think,18 has an excellent chapter about Dreyfus, detailing his arguments and the rather contentious interactions between him and AI scientists. Because she covers that ground so well, I’ll concentrate on his ideas about the need for “embodiment” as described in a couple of his recent papers. E. “Being There” Is Needed Dreyfus’s main point, I think, is that intelligence in humans derives from their “being in the world” and not because they are guided by rules. The use of rules in AI programs (as in humans) might allow competent behavior but not expert behavior. Here are some excerpts from an address Dreyfus gave in 2005:19 . . . in our formal instruction we start with rules. The rules, however, seem to give way to more flexible responses as we become skilled. . . . The actual phenomenon suggests that to become experts we must switch from detached rule-following to a more involved and situation-specific way of coping. . . . In general, instead of relying on rules and standards to decide on or to justify her actions, the expert immediately responds to the current concrete situation. . . . “Expert Systems” based on the rules so-called knowledge engineers elicited from experts were at best competent. It seems that, instead of using rules they no longer remembered, as the AI researchers supposed, the experts were forced to remember rules they no longer used. Indeed, as far as anyone could tell, the experts weren’t following any rules at all. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 391
- 392. 24 Speed Bumps According to Dreyfus, the transition from merely competent behavior to expert behavior requires “being in the world” through having a body embedded in the world. Embodied agents, such as humans, “can dwell in the world in such a way as to avoid the infinite task of formalizing everything” (as AI programs futilely attempt to do).20 Dreyfus’s view of this need for embodiment is based on a branch of a philosophical school called “phenomenology.” Dreyfus wrote me that the existential phenomenology of Martin Heidegger (1989–1976), which stresses “our practical involvement with people and things as our basic way of being,” is the basis for his critique of GOFAI.21 He argues that for AI to succeed it would need22 . . . a model of our particular way of being embedded and embodied such that what we experience is significant for us in the particular way that it is. That is, we would have to include in our program a model of a body very much like ours with our needs, desires, pleasures, pains, ways of moving, cultural background, etc. Others arguing for embodiment point out that some of the “computations” needed by an intelligent agent could be accomplished by the dynamic interactions between parts of its body and its environment. For example, Rolf Pfeifer, Max Lungarella, and Fumiya Iida have written that “An embodied perspective, because it distributes control and processing to all aspects of the agent (its central nervous system, the material properties of its musculoskeletal system, the sensor morphology, and the interaction with the environment), provides an alternative avenue for tackling the challenges faced by robotics. The tasks performed by the controller in the classical approach are now partially taken over by morphology and materials in a process of self-organization. . . ”23 But even if a body were needed, its form would seem to depend on what the associated AI system is used for. The body of the fictional HAL 9000 was the entire spacecraft that it controlled. Shakey the robot had a body that was apparently appropriate for its needs. If ever a “conversational Google” were to be developed that could engage in dialogs with users about the content of all Web pages, its “body” would be the entire Internet and the routines needed to access it. 24.1.3 Differences between Brains and Computers In addition to Dreyfus, several critics of AI have pointed out that “the brain is not a computer,” and, therefore, people who are attempting to do with computers what brains can do must necessarily fail. These critics often stress distinctions such as the following: • Computers have perhaps hundreds of processing units whereas brains have trillions. 392 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 393. 24.1 Opinions from Various Onlookers • Computers perform billions of operations per second whereas brains need perform only thousands. • Computers are subject to crashes whereas brains are fault tolerant. • Computers use binary signals whereas brains work with analog ones. • Computers do only what their programmers tell them to do whereas brains are creative. • Computers perform serial operations whereas brains are massively parallel. • Computers are constrained to be “logical” whereas brains can be “intuitive.” • Computers are programmed whereas brains learn. Aside from the fact that many of these distinctions are no longer valid,24 comparisons depend on what is meant by “the brain” and what is meant by “a computer.” If our understanding of the brain is in terms of its component neurons, with their gazillions of axons, dendrites, and synaptic connections, and if our understanding of a computer is in terms of serial, “von Neumann–style” operation – reading, processing, and writing of bits – all accomplished by transistor circuitry, well then of course, the brain is not that kind of a computer. However, we don’t understand “computation” by reference only to a low-level, von Neumann–style description. We can understand it at any one of a number of description levels. For example, computation might be understood as a very large number of concurrently active “knowledge sources” asynchronously reading from, transforming, and writing complex symbolic expressions on a “Blackboard” or as a collection of symbol-processing and neural network demons arranged in a Pandemonium-style network. Perhaps our gradually increasing understanding of how the brain operates will even lead to other useful computational models. Ideas about what “computation” can be are ever expanding, so those who would claim that the brain is not a computer will need to be more precise about just what kind of computer the brain is not. (After all, if some people, like Lucas, can restrict what a machine can be, it seems only fair that others can expand the definition of what a computer can be.) 24.1.4 But Should We? Besides the criticisms of AI based on what people claim it cannot do, there are also criticisms based on what people claim it should not do. Some of the “should-not” people mention the inappropriateness of machines attempting to Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 393
- 394. 24 Speed Bumps perform tasks that are inherently human-centric, such as teaching, counseling, and rendering judicial opinions. Others, such as the Computer Professionals for Social Responsibility mentioned previously, don’t want to see AI technology (or any other technology for that matter) used in warfare or for surveillance or for tasks that require experience-based human judgment. In addition, there are those who, like the Luddites of 19th century Britain, are concerned about machines replacing humans and thereby causing unemployment and economic dislocation. Finally, there are those who worry that AI and other computer technology would dehumanize people, reduce the need for person-to-person contact, and change what it means to be human. Joseph Weizenbaum (1923–2008; Fig. 24.4), the man who wrote the ELIZA program I mentioned in Section 2.3.3, has written and lectured about the dangers of giving computers responsibilities that he thought ought best be left to humans. Some say that the motivating reason for his concern was that he was surprised and shocked by the fact that some people mistook conversations with ELIZA for conversations with a real person. In his book Computer Power and Human Reason: From Judgment to Calculation,25 Weizenbaum argued that “there is a difference between man and machine, and. . . there are certain tasks which computers ought not be made to do, independent of whether computers can be made to do them. In his book, Weizenbaum stressed the importance of the cultural milieu in which a person grows up, lives, and works. No machine experiences (or could experience) a human-type background, and therefore no machine should be allowed to make the kinds of decisions or give the kinds of advice that require, among other things, the compassion and wisdom engendered by such a background. He emphasizes this point by saying that inexperience with these “domains of thought and action” would also apply “to the way humans relate to one another as well as to machines and their relations to man.”26 Thus, I suppose he would think that just as it would be inappropriate for a machine to make judicial decisions, so also would it be inappropriate for a person raised in America to make judicial decisions in Japan. Moreover, he ridicules the idea that a machine could obtain the necessary background by giving it a human-like body and sensory apparatus. He wrote that “the deepest and most grandiose fantasy that motivates work on artificial intelligence. . . is nothing less than to build a machine on the model of man, a robot that is to have its childhood, to learn language as a child does, to gain its knowledge of the world by sensing the world through its own organs, and ultimately to contemplate the whole domain of human thought.”27 Weizenbaum escaped from Nazi Germany with his family in 1936. That experience cannot but have sharpened his keen sense of social responsibility. He wrote, for example, that28 The very asking of the question, “What does a judge (or a psychiatrist) know that we cannot tell a computer?” is a monstrous 394 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 395. 24.1 Opinions from Various Onlookers Figure 24.4: Joseph Weizenbaum. (Photograph courtesy MIT Museum.) obscenity. That it has to be put in print at all, even for the purpose of exposing its morbidity, is a sign of the madness of our times. . . . [The relevant issues] cannot be settled by asking questions beginning with “can.” The limits of the applicability of computers are ultimately statable only in terms of oughts. What emerges as the most elementary insight is that, since we do not now have any ways of making computers wise, we ought not now to give computers tasks that demand wisdom. Even though Weizenbaum hedges a bit on the “can” question, I believe he really believed that machines “could not” as well as “should not.” For if machines really could make judgments with all of the “compassion and wisdom” with which humans can, why shouldn’t they? In addition to the concern about using any technology for antisocial purposes (such as war), the real danger, I think, lies in the premature use of machines: thinking that they are able to perform a task before they are really competent to do so. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 395
- 396. 24 Speed Bumps Another person who recoiled from the prospect of machines “taking over” is the physician, biologist, and essayist Lewis Thomas (1913–1993). In one of his celebrated columns, “Notes of a Biology-Watcher,” in The New England Journal of Medicine, he wrote29 The most profoundly depressing of all ideas about the future of the human species is the concept of artificial intelligence. The ambition that human beings will ultimately cap their success as evolutionary overachievers by manufacturing computers of such complexity and ingenuity as to be smarter than they are, and that these devices will take over and run the place for human betterment or perhaps, later on, for machine betterment, strikes me as wrong in a deep sense, maybe even evil. . . . This is what the artificial intelligence people are talking about: a mechanical brain with the capacity to look back over the past and make accurate predictions about the future, then to lay out flawless plans for changing that future any way it feels like, and, most appalling of all, capable of feeling like doing one thing or another. . . . It is, in my view, an absolutely hideous prospect, and if I thought it were really something waiting ineluctably ahead of us I would spend all my days in protest. Although there have been several other authors who have warned about the dangers of the inappropriate use of computers in general and of intelligent machines in particular, I’ll mention just one more, a self-confessed “neo-Luddite.” Theodore Roszak (1933– ) is a prominent author and social thinker – one well worth reading in my opinion. In his book The Cult of Information,30 he claimed that a growing cult, infatuated with “information” and “information processing,” is having debilitating cultural effects – “especially when it comes to teaching the young.” Roszak wrote that he is “an ally of all those serious students and users of information technology who hold a reasonably balanced view of what computers can and cannot, should and should not, do,” but claims that “the creation of a mystique of information [has made] basic intellectual discriminations between data, knowledge, judgment, imagination, insight and wisdom impossible.”31 Claiming that there is a “vital distinction” between information processing and thinking, he wrote32 Because the ability [of the computer] to store data somewhat corresponds to what we call memory in human beings, and because the ability to follow logical procedures somewhat corresponds to what we call reasoning in human beings, many members of the cult 396 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 397. 24.1 Opinions from Various Onlookers [of information] have concluded that what computers do somewhat corresponds to what we call thinking. However, Roszak concludes, computers cannot really “think.” The danger is that those who are persuaded (or duped into believing) that they can might inappropriately employ computers in tasks that require thinking and not just “data processing.” I believe Roszak has a legitimate concern here – AI is not yet up to all of the tasks to which we might try to put it. But Roszak also wrote33 There is no possibility that computers will ever equal or replace the mind except in those limited functional applications that do involve data processing and procedural thinking. The possibility is ruled out in principle, because the metaphysical assumptions that underlie the effort are false. Here, I disagree. I know of no “metaphysical assumptions” of AI other than that the brain is a kind of machine and therefore we ought to be able to understand it and build something that works very much like it. Furthermore, I know of no credible evidence that that metaphysical assumption is false. Although he does not think that computers can become minds, he worries about the additional danger that “it is possible to redefine the mind and its uses in ways that can be imitated by machine. Then we have a mechanical caricature which levels the activity down to a lower standard.”34 Roszak does have at least two good things to say about AI – one a negative result and one a positive contribution. As for the negative result, he says35 There is an ironic but highly valuable quality to AI in all its forms. The effort to simulate or surpass human intelligence is uncovering subtleties and paradoxes about the human mind we might never have imagined. By way of heroic failures, AI is teaching us how truly strange real intelligence is. On the positive side he comments that36 One field of AI, however, has made remarkable progress. . . Often, by quizzing specialists closely about their work, computer programmers can tease out procedures, assumptions, values that can then be formally specified. The result is an Expert System, one of the few practical applications of AI. Edward Feigenbaum sees such systems as the gateway to the next era of machine intelligence; he calls it “knowledge processing,” as opposed to mere Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 397
- 398. 24 Speed Bumps data processing. Whatever he may mean by “knowledge,” it surely represents a more complex approach to thinking than once prevailed in the field. 24.1.5 Other Opinions In January 1981, to sample some opinions about AI for a talk I was planning, I wrote to some leaders in computer science and related disciplines asking them what they thought about AI’s achievements, weaknesses, and prospects. I received several replies and will excerpt some comments.37 The computer scientist (and my colleague at Stanford) Donald Knuth wrote I’m intrigued that AI has by now succeeded in doing essentially everything that requires “thinking” but has failed to do most of what people and animals do “without thinking” – that, somehow, is much harder! I believe the knowledge gained while building AI programs is more important than the use of the programs. . . John R. Pierce, whom I have already mentioned in connection with both the ALPAC report on machine translation (in Section 7.2) and his negative comments about speech understanding (p. 282), wrote me a very short letter in which he stated Concerning artificial intelligence, I believe I invented the slogan, “Artificial intelligence is real stupidity.” . . . I resent artificial intelligence because I feel that it is unfair to computers. But then, artificial intelligence people did devise LISP, which is pretty good. The letter did not elaborate either on the slogan38 or why AI is “unfair to computers.” The Dutch computer scientist Edsger W. Dijkstra (1930–2002) was famous for many innovations in computer science, including an algorithm for finding the shortest (or least-costly) paths in graphs. He also championed what is called “structured programming,” a methodology that greatly improved the efficiency of writing (and understanding) programs. In response to my letter, he wrote (most cordially and in beautiful penmanship) To the artificial intelligentsia that argue “But we are only symbol manipulating machines, aren’t we?” one can only answer “There is 398 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 399. 24.2 Problems of Scale none so blind as them that won’t see!” The analogy is so shallow that I can characterize an appeal to it only as typically medieval thinking. In addition to concerns about AI’s “overstated claims,” many computer scientists thought AI to be a kind of “fringe activity” that did not adhere to rigorous scientific standards and a field that housed charlatans. I recall that when I first interviewed for a position at SRI in 1961, I was warned by one researcher there against joining research on neural networks. Such research, he claimed, was “premature,” and my involvement in it could damage my reputation. Concern for “respectability” has had, I think, a stultifying effect on some AI researchers. I hear them saying things like, “AI used to be criticized for its flossiness. Now that we have made solid progress, let us not risk losing our respectability.” One result of this conservatism has been increased concentration on “weak AI” – the variety devoted to providing aids to human thought – and away from “strong AI” – the variety that attempts to mechanize human-level intelligence. This is too bad, because, although I think the goals of weak AI are important and worthy, building an artifact that mimics the abilities of the human brain would be a tremendous scientific achievement – well worth the risk and not at all an “obscenity,” “evil,” “hideous,” nor “impossible in principle.” 24.2 Problems of Scale 24.2.1 The Combinatorial Explosion Because search plays such a prominent role in artificial intelligence, it is important to say something about how extremely difficult search problems can be. A typical search problem is usually cast as growing a “tree” of nodes, such as Arthur Samuel’s checkers game tree shown in Section 5.4 or the sliding-tile (eight-puzzle) search tree shown in Section 5.1. For example, if each node in a search tree has three possible “child” nodes (that is, a “branching factor” of 3), the top part of the tree would look like the one in Fig. 24.5. The “first level” of the tree has three nodes, the second has nine nodes, and so on. In the general case, for a tree with branching factor b, the dth level would have bd nodes (that is, b multiplied by itself d times). The total number of nodes that a search process would generate if it generated a whole tree with branching factor b down to and including all of the nodes at depth level d can be calculated to be b (b−1) (bd − 1). Readers who recall their high school algebra will recognize these expressions as “exponential” functions of d. Because the number of nodes in a search tree is an exponential function of its depth, search is called an exponential process. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 399
- 400. 24 Speed Bumps Figure 24.5: A search tree. If a program had to search a tree with branching factor of 3 to a depth level of 10 to find a goal, it would have to generate 88,572 nodes. Numbers like that were well within the range of the capabilities of computers of the 1960s and 1970s, and so they were quite capable of solving some of the simpler AI “toy problems.” But more realistic problems would involve search trees of much higher branching factors, having goals at much greater depth levels. For example, to search a tree with branching factor of 10 to depth 20 (a tree corresponding to only a modestly difficult search problem) would require the generation of more than 1020 nodes, a quite impossible feat. (1020 is one followed by 20 zeros, that is, 100 billion billion.) The difficulty of such searches has two aspects: computing time and storage space. Considering just computing time for a moment, even if we could generate a billion nodes each second (which is perhaps just barely thinkable), it would still take 100 billion seconds (over 3,000 years) to generate the tree with the branching factor and depth we have just been considering. Regarding storage space, even personal computers these days come with lots of it – 100 gigabytes (100 billion bytes) is typical. Assuming that a single node requires about one byte, one would need the storage equivalent of around a billion such computers for even our modestly sized search tree. The exponential nature of search means that as a problem’s size increases (as measured either by the branching factor or by the depth of search trees), the computational difficulty needed to solve the problem increases drastically – creating what is called a “combinatorial explosion.” 400 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 401. 24.2 Problems of Scale Of course, even early AI researchers knew about combinatorial explosions. That’s the reason for their interest in heuristics. Unfortunately, heuristics do not change the exponential character of search – at best they reduce the branching factor. The “explosion” still happens – just not quite so rapidly. For example, reducing the branching factor from 10 to 4 and searching again to depth 20 would still require the generation of over 420 or, roughly, one trillion nodes. AI critics have focused on this problem in their pessimistic assessments of AI’s achievements and prospects. For example, Sir James Lighthill (in his report that I mentioned in Chapter 16) wrote that “one rather general cause for the [AI] disappointments that have been experienced [is a] failure to recognize the implications of the ‘combinatorial explosion.’” Lighthill’s report did cause, as I have already mentioned, funding difficulties for AI research in Britain. 24.2.2 Complexity Theory AI researchers are not the only people who are concerned about the computational difficulty of problem solving. A branch of computer science called “complexity theory” deals with how long and how much storage space different kinds of programs might take (in the worst case) to solve different kinds of problems. They ask, among other things, “How does the size of a problem affect the time and space required to solve it?” Let’s look at some examples. The time that it would take for a computer program to find the largest number in a list of numbers is proportional to the size of this problem, namely, the number of items in the list. (The worst case would happen if the largest item happened to be the last item in the list; a program would then have to examine each item in the list.) Such a program is said to take “linear time.” Similarly, a program for finding out whether a given item is a member of a list of items would take linear time. (Again, the worst case would happen if the item happened to be the last item in the list.) In both cases, if we were to double the size, we would double the time required. Sorting a list of names, putting them in alphabetical order for example, is a harder problem. Programs for sorting lists differ in how long they take and how complicated they are to program. Reasonably simple sorting programs take time that is proportional to the square of the number of items in the list. That is, for these programs, sorting a list that has 100 items in it would take four times as long as a list with only 50 items in it. (Multiplying the number of elements by two increases the sorting time by two squared, or four. Sorting can actually be done faster. There are programs that can sort a list in time proportional to the logarithm of the size times the size.) Programs that can (in the worst case) take time proportional to a problem’s size, or the square, or the cube, or other “powers” of the size, run in what is called “polynomial time.” Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 401
- 402. 24 Speed Bumps Problems themselves can be graded according to the complexity of the least complex programs that are able to solve them. For example, if a program exists that can solve a problem in linear time, but none could solve it faster, than that problem is said to be of “linear complexity.” Finding out whether or not an item is a member of a list of items is a problem of linear complexity. We denote by P the class of problems that can be solved in polynomial time. Members of this class include calculating the greatest common divisor and determining whether or not a number is a prime number. Unfortunately, as I have already mentioned, the search procedures used in many AI programs require running times that are exponential in the size of the problem. For example, searching a tree having branching factor of b to depth d would take time proportional to bd . Using my previous example, we see that searching a tree with branching factor of 3 to a depth level of 10 requires the generation of 88,572 nodes. But doubling the depth to 20 would require searching not four times 88,572 nodes (which would be the case if search time was proportional to the square of the depth) but 88,572 × 88,572 or almost eight-billion nodes. Exponential complexity is much, much worse than polynomial complexity! The American computer scientist Stephen Cook (1939– ) [as well as the Russian, and now American, computer scientist Leonid Levin (1948– )] made major contributions to our understanding of the complexity of problems and the programs used to solve them. In particular, Cook (and Levin independently) defined the class called the nondeterministic polynomial, or NP, class of problems. This is the class of problems for which a candidate solution can be checked (but not necessarily solved) in polynomial time. For example, a proposed sequence of moves to solve a sliding tile puzzle can be checked to see whether it actually solves the puzzle in polynomial time, but (as far as is known) it would require exponential time to find a solution. So sliding tile problems, along with many other AI problems, belong to the class NP.39 It is not known whether or not there might be polynomial programs for solving the problems in the class NP. If there were, NP would equal P. So far, programs for solving problems in the class NP require exponential time (in the worst case). Whether or not NP equals P is one of the most famous unsolved problems in computer science. Many people think NP does not equal P because otherwise we would have found out by now. (The Clay Mathematics Institute has offered a prize of $1,000,000 for a solution of the P versus NP question – that is, showing either that they are equal or not equal.)40 24.2.3 A Sober Assessment These results in complexity theory caused some people to have grave doubts about the prospects for artificial intelligence. One of the most penetrating, and to my mind intelligent, assessments was written by the mathematician and computer scientist Jacob T. Schwartz (1930–2009; Fig. 24.6), whom I 402 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 403. 24.2 Problems of Scale have already mentioned in connection with his tenure at DARPA. In his article titled “The Limits of Artificial Intelligence,”41 Schwartz wrote that the extraordinary powers of the human brain arose from the way it used its prodigious computational and storage abilities “to organize information presented in relatively disordered form into internally organized structures on which sophisticated, coherent courses of symbolic and real-world action can be based.” To rival the brain, what AI needs, and what AI has vainly been trying to achieve, Schwartz claimed, are “coherent structures capable of directly guiding some form of computer action. . . generated automatically from relatively disorganized, fragmented input.” The ability to generate such “organized structures” would constitute a tremendous breakthrough because computers are “already enormously superhuman” in solving problems for which they can “accept, retain, and utilize fully structured material.” “If the basic obstacle posed by the need to program [computers] in detail could be overcome,” he wrote, “computers could ingest the information contained in all the world’s libraries and use this information with superhuman effectiveness.” (Nowadays, of course, besides libraries there is all the information, and misinformation, on the World Wide Web.) Figure 24.6: Jacob (Jack) Schwartz. (Photograph used with permission of Diana Schwartz.) The methods that AI researchers had used for automatically creating Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 403
- 404. 24 Speed Bumps structure from fragmented input were all based on heuristic search – either searching for chains of logical deductions or for paths in trees (and more generally graphs). Logical deductions had been used (among other things) to answer questions, to provide expert advice, and to generate programs and plans. Searching for paths in graphs had been used to prove theorems in geometry, to parse sentences, and to produce plans for robots. All of these are instances of creating structure (to use Schwartz’s term) from unstructured input. These “successes” however were all achieved on either trivially small problems or on ones whose subject matter was strictly circumscribed. Unfortunately, as Schwartz correctly claimed, all attempts to date to generate “broadly useful symbolic structures from more disorganized and fragmentary input” have invariably been defeated by the combinatorial explosion. He summarized the situation by echoing Dreyfus’s charge that “. . . the history of AI research to date [consisted] always of very limited success in particular areas, followed immediately by failure to reach the broader goals at which these initial successes seem at first to hint. . . ” Schwartz’s opinions about AI did have consequential effects because, as I mentioned in Section 23.4, he was the Director of the DARPA ISTO from 1987 to 1989 and presided over some cutbacks in AI research (including the cancellation of one of my own research projects in 1987). Even though he was generally dismissive of AI work, Schwartz did write that those “areas of AI to which classical scientific and algorithmic techniques apply can be expected to progress more rapidly than areas that deal with deeper problems for which only less focused approaches are available.” As one example, he cited the problem of determining “whether one or more objects of known shape moving in an environment containing obstacles of other known shapes can pass from one specified position to another without colliding either with the obstacle or with each other.” Although these results from complexity theory did constitute one of the “speed bumps” in AI’s rapid forward progress, AI researchers quickly recovered and found various ways around the combinatorial explosion problem. They pointed out, for example, that complexity results were based on worst-case performance, and solutions might often be found faster than in the worst case. For example, I’ll mention the work of Richard Korf (1956– ; Fig. 24.7), an AI researcher at the University of California at Los Angeles. Korf is well known for his work on tackling extremely difficult search problems. He often uses the sliding tile and other puzzles as laboratory “drosophila” for exploring new ideas in search. You will recall that I used the example of a sliding tile puzzle to illustrate heuristic search processes. The one I used consisted of eight tiles in a 3 × 3 array; the classic version consists of fifteen tiles in a 4 × 4 array. One can imagine larger versions, such as twenty-four tiles in a 5 × 5 array. The 4 × 4 puzzle already presents a quite challenging problem for heuristic search. In fact, in his comments about the scaling difficulties of search processes, Jack 404 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 405. 24.2 Problems of Scale Figure 24.7: Richard Korf. (Photograph courtesy of Richard Korf.) Schwartz wrote “. . . the graph of states [of the 3 × 3 puzzle] consists of 9!, or 362,880 [nodes], so even for so simple a problem brute-force graph search begins to become taxing. For the corresponding 4 × 4 puzzle, whose state space involves 16!, or over 1013 , nodes, it is completely infeasible.” [Actually, Schwartz was off by (an inconsequential) factor of 2 in both cases. It has been known since 1879 that if you start from any particular initial configuration, you can only reach 1/2 of all the possible configurations.42 Thus, for the eight-tile version, the entire state-space graph consists of two separate disconnected graphs of size 9!/2 = 181, 440.] Yet, not only has Korf written heuristic search programs to solve instances of the 4 × 4 puzzle, but in an abstract of a 1996 paper he and his co-author wrote 43 We have found the first optimal solutions to a complete set of Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 405
- 406. 24 Speed Bumps random instances of the Twenty-Four Puzzle, the 5 × 5 version of the well-known sliding-tile puzzles. Our new contribution to this problem is a more powerful admissible heuristic function. . . . [W]e observe that as heuristic search problems are scaled up, more powerful heuristic functions become both necessary and cost-effective. The search space for the 5 × 5 puzzle is about a trillion times larger than that of the 4 × 4 puzzle. It has about 7.756 ×1024 nodes. Search is also made easier if one could be satisfied with “good” or approximate solutions to problems rather than insisting on the “best” solutions. In a subsequent chapter, I’ll describe some rather remarkable progress on dealing with large search problems; this progress has flattened the speed bumps a bit. Even with these ways around the complexity results, however, AI people did begin to acknowledge other shortcomings – a subject I’ll turn to next. 24.3 Acknowledged Shortcomings As AI researchers began to confront problems of practical importance, they themselves had to acknowledge several difficulties. These came up in several application areas. I’ll mention just a few. In attempting to prove nontrivial mathematical theorems, for example, theorem-proving programs quickly exhausted the space necessary to store intermediate results. But humans (well, some humans at least) are able to prove theorems. What methods are they using that computer programs are not? Mathematicians would probably say that intuition, judgment, experience, mathematical sophistication, and such are critical to their successes. So far, it has proven difficult to provide computers with these capabilities. In game playing, although MAC HACK VI and CHESS 4.6, to name two examples, played pretty good chess, they were far from being able to beat world champions. In fact, in August 1978 at the Canadian National Exhibition in Toronto, David Levy defeated the reigning Computer Chess Champion, CHESS 4.7, from Northwestern University, thus winning the bet he made ten years before against John McCarthy and Donald Michie. As Levy put it, “I managed to beat the program fairly convincingly, by three wins to one with one game drawn (the sixth game did not need to be played) and with this match I won my bet.”44 What accounts for championship ability? It’s unlikely that chess champions look at more chess positions than computers do. However, they 406 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 407. 24.3 Acknowledged Shortcomings probably look farther ahead in the game tree along the most important branches. Their experience seems to enable them to evaluate the potential of those candidate positions that are worthy of exploration and to ignore further exploration of worthless positions. Perhaps they also think in a more strategic manner than do chess programs that consider individual chess moves. Another game, perhaps more challenging for humans even than chess, is the game of Go – a board game that originated in China more than 4,000 years ago and is very popular in Asian countries today. In Go, two players alternate in placing black and white stones at the intersections of a 19 × 19 grid of two sets of lines ruled on a board. I won’t describe the rules of the game here, except to say that (at least in the early stages of the game) a player is faced with the problem of deciding at which of almost 361 (19 × 19) positions to place a stone. Even for the most powerful search processes, searching a tree, each of whose nodes has nearly 361 immediate descendants, is out of the question. Human players must be using other strategies, and, whatever these strategies are, they are still unknown to AI researchers. Many AI researchers think that performance in Go is a better measure of AI’s abilities than is performance in chess. I’ll describe some recent progress later in the book. Although expert systems reason usefully (and even with economic advantage) about specific problems in medicine, geology, chemistry and other delimited areas, they are acknowledged to be “brittle” – that is, they break down when confronted with problems outside their area of expertise or even on problems within their area of expertise if knowledge were needed that had not been provided in their rules. They don’t know what they don’t know and therefore might provide wrong answers in cases where a human expert would do better. It is said that John McCarthy, in an interaction with the medical expert system MYCIN, typed in some information about a hypothetical patient, saying that he was male and also saying that he underwent amniocentesis. MYCIN accepted all of that without complaint! That male patients don’t get pregnant was not considered part of the “expert knowledge” that MYCIN needed to be given. One of the reasons why expert systems are brittle is that they lack “common sense.”45 In addition to the expert knowledge that humans might acquire through education and professional experience, they also have a lot of general knowledge. They know, for example, that only females can become pregnant, that umbrellas protect against sun and rain, that certain birds migrate, that food can be purchased in markets, and millions upon millions of other facts. Benjamin Kuipers, an AI researcher and professor at the University of Michigan (formerly at the University of Texas at Austin), defined common sense this way: “Commonsense knowledge is knowledge about the structure of the external world that is acquired and applied without concentrated effort by any normal human that allows him or her to meet the everyday demands of the physical, spatial, temporal and social environment with a reasonable degree of success.”46 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 407
- 408. 24 Speed Bumps This general knowledge is acquired gradually as children grow into adults and as adults mature. For example, a child probably does not know that small tablets in little plastic bottles might be dangerous if swallowed (which is why these bottles have child-proof caps), teenagers know a lot of things that eight-year olds typically do not, and the knowledge that enables a reader of The New Yorker, say, to understand its reviews of books and films goes beyond what a teenager typically knows. Also, of course, people in different countries and cultures will have different common-sense knowledge. It seems to me that the knowledge of any particular human should be thought of as an ever-growing tree whose base and lower branches comprise “common sense” and whose upper ramifications comprise the “expertise” of specialized disciplines that the person might have learned. The tree metaphor is also useful in emphasizing the point that the knowledge in the upper branches uses concepts that occur in the trunk and lower branches. We saw in an earlier chapter that a full understanding of sentences in natural language seemed to require the common-sense information that humans have but computers still do not. The daunting prospect of endowing computers with common sense has led to two quite opposite reactions. Some see this difficulty as ruling out the possibility of AI (or at least of strong AI) for the foreseeable future. Others, though, say “Let’s get on with it.” (I’ll be talking about the work of one of the let’s-get-on-with-it people in a subsequent chapter.) Of course, it is no more to be expected that any particular AI system will understand all natural language sentences than it can be expected that any particular human will understand natural language sentences about all subjects. Humans have their limitations, and AI programs will have them too. That prospect should no more limit our attempts to produce intelligent programs than it does to educate intelligent humans. 24.4 The “AI Winter” During the early 1980s, many AI sponsors, in government and in industry, had greatly inflated expectations of what AI could do. Undoubtedly, some of the blame for their unjustified optimism could be placed on AI researchers themselves who were motivated to make exaggerated promises. The failure to deliver systems matching these unrealistic hopes, together with the accumulating critical commentary that I have already mentioned, combined in the mid- to late 1980s to bring on what came to be called an “AI winter.” Indeed, at the 1984 AAAI National Convention several leading AI researchers warned about this possibility during a panel session titled “The ‘Dark Ages’ of AI – Can We Avoid Them or Survive Them?” The panel’s chair, Drew McDermott of Yale University, started the session off by saying47 In spite of all the commercial hustle and bustle around AI these 408 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 409. 24.4 NOTES days, there’s a mood that I’m sure many of you are familiar with of deep unease among AI researchers who have been around more than the last four years or so. This unease is due to the worry that perhaps expectations about AI are too high, and that this will eventually result in disaster. . . . I think it is important that we take steps to make sure the “AI Winter” doesn’t happen – by disciplining ourselves and educating the public. But if “disciplining” and “educating” did take place, they were insufficient to prevent the worried-about downturn. During the late 1980s, membership in the AAAI gradually fell. By 1996, it had leveled off to between 4,000 and 5,000 members. Advertising in the AI Magazine dropped also – as did participation by government and industry in AI conference exhibits. Several AI companies closed their doors, and AI research at some of the larger computer hardware and software companies was terminated. According to Alex Roland, between 1987 and 1989, DARPA’s budget for basic AI and Strategic Computing research fell from $47 million to $31 million. (Even so, according to Alex Roland, CMU’s budget was increased for its speech understanding program and its autonomous vehicle program during this time.) But the winter endured only for a season – a season not of hibernation but of renewed efforts to carry on. Several new ideas were explored, and older ones were strengthened with added powers, as I’ll explain in subsequent chapters. Notes 1. Hilary Putnam, “Much Ado about Not Very Much,” Dædalus (Special Issue on Artificial Intelligence), pp. 269–281, Winter 1988. [381] 2. Alan Ross Anderson (ed.), Minds and Machines, Englewood Cliffs, NJ: Prentice-Hall, Inc., 1964. [381] 3. John R. Lucas, “Minds, Machines, and Gödel,” Philosophy, Vol. XXXVI, pp. 112–127, 1961; reprinted in Alan Ross Anderson (ed.), Minds and Machines, pp. 43–59, Englewood Cliffs, NJ: Prentice-Hall, Inc., 1964; available online at http://users.ox.ac.uk/∼jrlucas/Godel/mmg.html. [382] 4. Lucas himself lists some references to these in a Web site, http://users.ox.ac.uk/∼jrlucas/Godel/referenc.html. [382] 5. John R. Lucas, The Freedom of the Will, Oxford: Oxford University Press, 1970. Lucas claims on his Web site that the book is out of print but is now available from Oxford University Press “on a one-off basis.” [382] 6. Available online at http://users.ox.ac.uk/∼jrlucas/Godel/brighton.html. [382] 7. Mortimer Taube, Computers and Commonsense: The Myth of Thinking Machines, New York: Columbia University Press, 1961. [383] 8. Roger Penrose, The Emperor’s New Mind: Concerning Computers, Minds and the Laws of Physics, New York: Random House, Inc., 1989. [383] Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 409
- 410. 24 NOTES 9. Roger Penrose, Shadows of the Mind: A Search for the Missing Science of Consciousness, Oxford: Oxford University Press, 1994. [383] 10. Roger Penrose, “Beyond the Doubting of a Shadow: A Reply to Commentaries on Shadows of the Mind,” PSYCHE, Vol. 2, p. 23, January 1996. Available online (with pointers to articles by critics) at http://psyche.cs.monash.edu.au/v2/psyche-2-23-penrose.html. [383] 11. See John R. Searle, “Minds, Brains, and Programs,” Behavioral and Brain Sciences, Vol. 3, No. 3, pp. 417–457, 1980. Available online at http://www.bbsonline.org/documents/a/00/00/04/84/bbs00000484-00/bbs.searle2.html. [383] 12. Ibid. [384] 13. Allen Newell and Herbert A. Simon, “Computer Science as Empirical Inquiry: Symbols and Search,” Communications of the ACM. Vol. 19, No. 3, pp. 113–126, March 1976. Available online at http://www.rci.rutgers.edu/∼cfs/472 html/AI SEARCH/PSS/PSSH1.html. [387] 14. For an online interview with Dreyfus about his career, see http://globetrotter.berkeley.edu/people5/Dreyfus/dreyfus-con0.html. [389] 15. Hubert L. Dreyfus, “Alchemy and Artificial Intelligence,” RAND paper P-3244, The RAND Corporation, Santa Monica, CA, December 1965. Available online at http://www.rand.org/pubs/papers/2006/P3244.pdf. [389] 16. John Haugeland, Artificial Intelligence: The Very Idea, Cambridge, MA: MIT Press, 1985. [391] 17. See, for example, Hubert L. Dreyfus, What Computers Can’t Do: A Critique of Artificial Reason, New York: Harper & Row, 1972 (second edition, 1979). A revised edition with the title What Computers Still Can’t Do: A Critique of Artificial Reason was published by MIT Press, 1992. Hubert L. Dreyfus and Stuart E. Dreyfus, Mind Over Machine: The Power of Human Intuition and Expertise in the Era of the Computer, New York: Free Press, 1986. [391] 18. Pamela McCorduck, Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence, San Francisco: W. H. Freeman and Co., 1979. [391] 19. Hubert L. Dreyfus, “Overcoming the Myth of the Mental: How Philosophers Can Profit from the Phenomenology of Everyday Expertise,” Presidential Address, Proceedings and Addresses of the American Philosophical Association, Vol. 79, No. 2, November 2005. Available online at http://socrates.berkeley.edu/∼hdreyfus/pdf/Dreyfus%20APA% 20Address%20%2010.22.05%20.pdf. [391] 20. Hubert L. Dreyfus, What Computers Still Can’t Do: A Critique of Artificial Reason, p. 255, Cambridge, MA: MIT Press, 1992. [392] 21. E-mail correspondence of August 9, 2007. For Dreyfus’s comments about Heidegger, see Hubert L. Dreyfus, Being-in-the-World: A Commentary on Heidegger’s Being and Time, Division I, Cambridge, MA: MIT Press, 1991. [392] 22. Hubert L. Dreyfus, “Why Heideggerian AI Failed and How Fixing It Would Require Making It More Heideggerian,” (a paper written in connection with being awarded the APA’s Barwise Prize, 2006), Philosophical Psychology, Vol. 20, No. 2, pp. 247–248, 2007; reprinted in Michael Wheeler (ed.), The Mechanization of Mind, Cambridge MA: MIT Press, in press. [392] 23. Rolf Pfeifer, Max Lungarella, and Fumiya Iida, “Self-Organization, Embodiment, and Biologically Inspired Robotics,” Science, Vol. 318, No. 5853, pp. 1088–1093, November 16, 2007. [392] 24. For example, a paper written in 2003 claimed that “Google’s architecture features 410 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 411. 24.4 NOTES clusters of more than 15,000 commodity-class PCs with fault-tolerant software.” Undoubtedly, Google uses many more networked computers today. See Luiz André Barroso, Jeffrey Dean, and Urs Hölzle, “Web Search for a Planet: The Google Cluster Architecture,” IEEE Micro, March–April 2003. Available online at http://labs.google.com/papers/googlecluster-ieee.pdf. [393] 25. Joseph Weizenbaum, Computer Power and Human Reason: From Judgment to Calculation, San Francisco: W. H. Freeman and Co., 1976. [394] 26. Ibid, pp. 223–224. [394] 27. Ibid, pp. 202–203. [394] 28. Ibid, p. 227. [394] 29. Lewis Thomas, “Notes of a Biology Watcher: On Artificial Intelligence,” The New England Journal of Medicine, Vol. 302, No. 9, pp. 506ff, February 28, 1980. [396] 30. Theodore Roszak, The Cult of Information: A Neo-Luddite Treatise on High-Tech, Artificial Intelligence, and the True Art of Thinking, second edition, Berkeley, CA: University of California Press, 1994. [396] 31. Ibid, pp. xviii–xix. [396] 32. Ibid, p. xiv. [396] 33. Ibid, p. 232. [397] 34. Ibid, p. 232. [397] 35. Ibid, p. xxiv. [397] 36. Ibid, p. 232. [397] 37. All responses are in my files. [398] 38. The online encyclopedia Wikipedia mentions the slogan in its entry for Pierce at http://en.wikipedia.org/wiki/John R. Pierce. [398] 39. For a proof about sliding tile puzzles, see Daniel Ratner and Manfred Warmuth, “Finding a Shortest Solution for the N*N-extension of the 15-puzzle Is Intractable,” Journal of Symbolic Computing, Vol. 10, pp. 111–137, 1990. [402] 40. See http://www.claymath.org/prizeproblems/index.htm. [402] 41. Jacob Schwartz, “Limits of Artificial Intelligence,” in Stuart C. Shapiro and David Eckroth (eds.), Encyclopedia of Artificial Intelligence, Vol. 1, pp. 488–503, New York: John Wiley and Sons, Inc. 1987. [403] 42. W. W. Johnson and W. E. Story, “Notes on the 15 Puzzle,” American Journal of Mathematics, Vol. 2, pp. 397–404, 1879. I thank Richard Korf for this citation. [405] 43. Richard E. Korf and L. A. Taylor, “Finding Optimal Solutions to the Twenty-Four Puzzle,” Proceedings of the Thirteenth National Conference on Artificial Intelligence and the Eighth Innovative Applications of Artificial Intelligence Conference, pp. 1202–1207, Menlo Park, CA: AAAI Press and Cambridge, MA: MIT Press, August 1996. [405] 44. David Levy, Robots Unlimited: Life in a Virtual Age, p. 84, Wellesley, MA: A. K. Peters, Ltd., 2006. [406] 45. See the paper by John McCarthy, “Some Expert Systems Need Common Sense,” Heinz Pagels (ed.), Computer Culture: The Scientific, Intellectual and Social Impact of the Computer, Annals of the New York Academy of Sciences, Vol. 426, November 1995. Available online at http://www-formal.stanford.edu/jmc/someneed/someneed.html. [407] 46. Benjamin Kuipers, “On Representing Commonsense Knowledge, in Nicholas V. Findler (ed.), Associative Networks: The Representation and Use of Knowledge by Computers, pp. 393–408, New York: Academic Press, 1979. Available online at Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 411
- 412. 24 NOTES ftp://ftp.cs.utexas.edu/pub/qsim/papers/Kuipers-csk-79.ps.gz. [407] 47. See Drew McDermott, M. Mitchell Waldrop, B. Chandrasekaran, John McDermott, and Roger Schank, “The Dark Ages of AI: A Panel Discussion at AAAI-84,” AI Magazine, Vol. 6, No. 3, pp. 122–134, Fall 1985. [408] 412 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 413. 25.1 Chapter 25 Controversies and Alternative Paradigms Difficulties such as those I have just written about rekindled a number of controversies among AI researchers themselves. Frustrated with AI’s slowdown, people with different approaches to AI eagerly stepped forward to claim that what AI needed was more of this or that alternative to AI’s reigning paradigm – the paradigm John Haugeland called “good-old-fashioned AI” or GOFAI. GOFAI, of course, had as its primary rationale Newell and Simon’s belief that a “physical symbol system has the necessary and sufficient means for intelligent action.” But GOFAI seemed to be running out of steam during the 1980s, making it vulnerable to challenges by AI researchers themselves – challenges that had to be taken more seriously than those of Searle, Dreyfus, Penrose, and others outside of the field. In this chapter I’ll describe some of these internal controversies and mention a few of the new paradigms that emerged. 25.1 About Logic Among the pursuers of the GOFAI approach were those who used logical representations and logical reasoning methods – ideas pioneered by John McCarthy. These people were sometimes called “logicists.” (I was among them, having co-authored a 1987 book titled The Logical Foundations of Artificial Intelligence.)1 Drew McDermott, a professor at Yale University (who received his Ph.D. from MIT), was one of those who began to have doubts about the role of logic in AI. This fact was significant because McDermott himself had been a prominent logicist, but in an influential 1987 paper he concluded that the premise that “. . . a lot of reasoning can be analyzed as deductive or approximately deductive, is erroneous.”2 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 413
- 414. 25 Controversies and Alternative Paradigms He went on to say Unfortunately, the more you attempt to push the logicist project, the less deduction you find. What you find instead is that many inferences which seem so straightforward that they must be deductions turn out to have nondeductive components. . . . Think of the last time you made a plan, and ask yourself if you could have proven the plan would work. Chances are you could easily cite ten plausible circumstances under which the plan would not work, but you went ahead and adopted it anyway Several people, logicists (including me) and near-logicists, were invited to submit “commentaries,” and these were published along with McDermott’s article. Have a look if you would like to sample one of the important controversies in AI. The discussions about the role of logic in artificial intelligence helped reshape AI’s use of logic, and, in extended form, it still serves as the primary means for representing declarative knowledge. 25.2 Uncertainty Another objection to the use of logical representations was based on the fact that logical sentences must be either true or false whereas so much of human knowledge is uncertain. Both MYCIN and PROSPECTOR (along with some other expert systems) were able to accommodate uncertainty – MYCIN with its “certainty factors” and PROSPECTOR with its use of probability values. Several other ideas for dealing with uncertainty have been proposed. I’ll mention two alternatives to the use of probabilities. One is the so-called Dempster–Shafer (D-S) theory for assigning degrees of belief to statements and for combining degrees of belief based on independent items of evidence.3 D-S theory has been used extensively in problems where data from several sources need to be combined (or “fused,” the term used by D-S people) to reach decisions.4 The other alternative to using probabilities is “fuzzy logic,” invented by the computer scientist Lotfi Zadeh (1921– ; Fig 25.1).5 Fuzzy logic allows truth values of statements to take on any value between 1 (certainly true) and 0 (certainly false). It is based on fuzzy set theory in which set membership can take on intermediate values between “in the set” and “not in the set.” That is, something can be “partially in the set.” Zadeh uses, as one example, the set of tall people. Depending on one’s definition of tall, John, say, who is 5 feet 10 inches (177.8 cm), might be described as being in the set “tall” to degree 0.7 Then the statement “John is tall” would have a truth value of 0.7. A truth value of 0.95, for example, might correspond to the statement “John is quite 414 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 415. 25.2 Uncertainty tall.” Modifiers such as “slightly,” “moderately,” and “very” are easily converted to fuzzy truth values for the statements using them. Here is how the truth values of combinations of statements are computed in fuzzy logic: If A and B are two statements, then the truth value of the “conjunctive” combination, (A And B), is the smaller of the truth values of A and of B. The truth value of the “disjunctive” combination, (A Or B), is the larger of the truth values of A and of B. Figure 25.1: Lotfi Zadeh. (Photograph courtesy of Lotfi Zadeh.) Zadeh points out that his truth values and set membership values cannot be construed as probabilities. His reasons need not concern us here; in any case, the matter is controversial. (Most statisticians claim that probability theory is the only mathematically rigorous way to deal with uncertainty.) Suffice it to say that there is an extensive literature on fuzzy logic and its several applications, especially in control systems.6 One oft-cited example of the use of fuzzy control is Maytag Company’s “IntelliSense” dishwasher. According to a press account,7 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 415
- 416. 25 Controversies and Alternative Paradigms Maytag Co., Newton, Iowa, has developed what it claims is the world’s first “intelligent” dishwasher. At the touch of a button, the computerized machine figures out the optimum wash cycle for any load. The dishwasher’s apparent ability to reason stems from fuzzy-logic control and an advanced sensor module that sits in the pump, measuring food particles, water temperature, detergent, and wash-arm rotation. In Zadeh’s view, fuzzy logic is one component of a larger effort in what he calls “soft computing” – a discipline that “. . . differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role model for soft computing is the human mind.”8 Contrasted with these alternatives to probability theory, the invention of Bayesian networks, to be described in a subsequent chapter, has revitalized methods based on probabilities for representing and reasoning with uncertain information. 25.3 “Kludginess” Another controversy concerned the very nature of the mechanism (or mechanisms) underlying intelligent behavior. Opposing those who sought some unitary general principle based on search or learning or logic or massive amounts of common-sense knowledge, Marvin Minsky claimed that intelligence (at least as exhibited by the human brain) was a “kludge.” (Among various dictionary definitions of “kludge” are the following: 1. A system, especially a computer system, that is constituted of poorly matched elements or of elements originally intended for other applications. 2. A clumsy or inelegant solution to a problem.) Minsky’s view was that intelligence resulted from perhaps hundreds or thousands of ad hoc, special-purpose mechanisms, loosely interacting, sometimes cooperating and sometimes competing, to solve the myriad problems faced by evolving humans. In Minsky’s words,9 The brain’s functions simply aren’t based on any small set of principles. Instead, they’re based on hundreds or perhaps even thousands of them. In other words, I’m saying that each part of the brain is what engineers call a kludge – that is, a jury-rigged solution to a problem, accomplished by adding bits of machinery wherever needed, without any general, overall plan: the result is that the human mind – which is what the brain does – should be regarded as a collection of kludges. The evidence for this is perfectly clear: If you look at the index of any large textbook of 416 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 417. 25.4 About Behavior neuroscience, you’ll see that a human brain has many hundreds of parts – that is, subcomputers – that do different things. Why do our brains need so many parts? Surely, if our minds were based on only a few basic principles, we wouldn’t need so much complexity. Of course, just because the brain is a kludge does not mean that computer intelligences have to be. Nevertheless, some AI researchers favored systems consisting of collections of experimentally derived, ad hoc routines designed to solve specific problems. These people called themselves “scruffies” to distinguish themselves from the “neats” who favored programs based on theoretically based principles. (These terms were apparently first used by Roger Schank in the 1970s to contrast his approach to building natural language processing systems with the more theoretically based work of McCarthy and others.) In his keynote address at the 1981 annual meeting of the Cognitive Science Society, Robert Abelson compared the two camps by saying “The primary concern of the neat is that things should be orderly and predictable while the scruffy seeks the rough-and-tumble of life as it comes. . . ”10 I believe that both neats and scruffies are needed in a field as immature as AI is. Scruffies are better at exploring frontiers outside the boundaries of well-established theory. Neats help codify newly gained knowledge so that it can be taught, written about, and thus remembered. 25.4 About Behavior 25.4.1 Behavior-Based Robots Using an approach that harkens back to Grey Walter’s “tortoises,” the MIT computer scientist Rodney Brooks eschewed complex representations and reasoning processes and focused instead on what he called a “behavior-based approach to building robots that operate in the real world.” Brooks wrote that his approach drew inspiration from attempting to [r]ecapitulate evolution, or an approximation thereof, as a design methodology, in that improvements in performance come about by incrementally adding more situation specific circuitry [or software organized like circuitry] while leaving the old circuitry in place, able to operate when the new circuitry fails to operate (most probably because the perceptual conditions do not match its preconditions for operating). Each additional collection of circuitry is referred to as a new layer. Each new layer produces some observable new behavior in the system interacting with its environment.11 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 417
- 418. 25 Controversies and Alternative Paradigms Figure 25.2: Rodney Brooks (top) and his Crawling Robot, Genghis (bottom). (Photographs courtesy of Rodney Brooks.) Genghis, shown in Fig. 25.2, was an early example of one of Brooks’s robots using layered circuitry. It was a six-legged robot about 35 cm long with a leg span of 25 cm and weighing about a kilogram. It was able to crawl over rough terrain and follow a person using its infrared sensors. (For a short movie of Genghis walking visit http://groups.csail.mit.edu/lbr/genghis/genghis-short2.mov.) Its sensors included two front “whiskers,” two inclinometers (to measure pitch and roll), and six forward-looking passive infrared sensors. The on-board circuitry controlling Genghis was built by adding modules, 418 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 419. 25.4 About Behavior one on top of another, incrementally. Each layer handled increasingly complex modes of walking and “subsumed” (overrided when appropriate) the layer below when the layer below was not able to handle the current situation. Brooks called this type of layered organization a “subsumption architecture.” The circuitry consisted of simple computational devices called “augmented finite-state machines” (implemented by 8-bit microprocessors).12 Unlike earlier symbolic approaches, Brooks’s approach to robotics did not use central models of the environment and programs to “plan” courses of action. He argued that “the symbol system hypothesis upon which classical AI is based is fundamentally flawed. . . ”13 Instead, Brooks wrote . . . the specific goals of the robot are never explicitly represented [in the behavior-based approach], nor are there any plans – the goals are implicit in the coupling of actions to perceptual conditions, and apparent execution of plans unroll in real-time as one behavior alters the robot’s configuration in the world in such a way that new perceptual conditions trigger the next step in a sequence of actions.14 In his paper “Elephants Don’t Play Chess,”15 Brooks gives examples of several other quite interesting robot systems developed in his MIT lab. The title of Brooks’s paper is meant to indicate that quite complex behavior (such as the behavior of elephants for example) can be achieved with systems that (presumably) do not have the representational and reasoning powers required for intelligent activities such as playing chess. Yet, although AI scientists would certainly be pleased to be able to build machines with the intelligence of elephants, achieving AI’s ultimate goals would seem to require complex representational and reasoning methods beyond what the behavior-based approaches are able to offer. Although I think that following along the path (or paths) of the evolution of ever-more capable and intelligent animals has a lot to recommend it, I don’t think we are very far along in going “from earwigs to humans” (to use the title of one of Brooks’s articles) – let alone in getting up to elephants.16 25.4.2 Teleo-Reactive Programs Even though it’s doubtless not the whole story, coordinating behavior with ongoing perceptual input is an important part of an intelligent system. It’s a part that I have been interested in ever since the days of working on intermediate-level actions for Shakey the robot. I was able to return to thinking about behavioral control during a sabbatical year in 1990 and 1991. I spent part of that year in Brooks’s laboratory at MIT. There, aided by some important suggestions made by a Stanford (and soon-to-be MIT) student, Mark Torrance, I developed what I called the “teleo-reactive” (T-R) Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 419
- 420. 25 Controversies and Alternative Paradigms programming language. (“Teleo” comes from the Greek word telos, meaning “end” or “purpose.”) A T-R program is an intermediate-level agent control program that robustly directs a robot toward a goal in a manner that continuously takes into account the robot’s perceptions of its dynamically changing environment. Perhaps you will tolerate a slight digression into how T-R programs operate. I use it to illustrate some of the issues that arise in controlling a purpose-driven robot. Here’s an example of a T-R program, one that controls a robot kicking a soccer ball. (The program is really very simple; you can try “running it” by hand after I explain how these kinds of programs work.) This program mimics how a beginning soccer player (say a six-year old) might go about kicking a soccer ball. Not heeding what else might be going on, he or she runs to get close to the ball, faces it, and then boots it away. kick(x): 1. Close(x) AND Facing(x) -> foot-swing 2. Close(x) -> face(x) 3. Facing(x) -> move-forward 4. True -> moveto(x) face(x): 1. Facing(x) -> do-nothing 2. Left(x) -> rotate-ccw 3. True -> rotate-cw moveto(x): 1. Close(x) -> do-nothing 2. Facing(x) -> move-forward 3. True -> face(x) There are three parts to this program, a main part, namely, kick(x), and two “subprograms,” namely, face(x) and moveto(x). To understand how it works, I’ll first describe two important sets of components, the “perceptual routines” and the “primitive action routines.” The perceptual routines determine whether or not some feature of the robot’s situation is true or false. The primitive action routines control the basic motor actions of the robot and are presumed to be “built into the robot” (much like “reflexes” are built into animals). • Perceptual Routines – Close(x) determines whether the robot is within “kicking distance” of x, where x can be anything at all. In programs like this, x is called a “parameter” or “variable” of the program. When the program is actually run, x will have a definite value, such as Ball, 420 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 421. 25.4 About Behavior the soccer ball. But using a parameter in the program instead of a definite value permits us to use the same program for different “instances” of x. – Facing(x) determines whether the robot is facing x. – Left(x) determines whether x is somewhere (anywhere) off to the left of the direction the robot is facing. When it is true, the robot should rotate counterclockwise to be facing x. • Primitive Action Routines – foot-swing is the basic action that moves the robot’s “foot” forward rapidly. If a ball happens to be in the way, the ball goes sailing. – move-forward makes the robot move in the direction it is facing. – rotate-ccw makes the robot rotate (in place) in a counterclockwise direction. – rotate-cw makes the robot rotate (in place) in a clockwise direction. The other action routines, namely, kick(x), face(x), and moveto(x) are not primitive but are composed of other programs. Note that the numbered lines of the T-R programs shown here consist of a part to the left of an arrow (->) and a part to the right of an arrow. The part to the left is called the “condition part” because it consists of a check to determine whether some condition is true. The part to the right is called the “action part.” My first step in explaining how T-R programs work in general is to show how kick(Ball) works in a specific situation. Let’s assume that the robot is facing the ball but is not close enough to it to kick it. The robot wants to kick the ball so it activates the program kick(x) with the parameter x set to Ball. Here then is the program that the robot activates: kick(Ball): 1. Close(Ball) AND Facing(Ball) -> foot-swing 2. Close(Ball) -> face(Ball) 3. Facing(Ball) -> move-forward 4. True -> moveto(Ball) Note how every appearance of x in the program is now replaced by Ball because it is the ball that is to be kicked. T-R programs are interpreted by looking at the lines of the program in numeric order and identifying the first line in the program whose condition part is true. The action part of that line is then activated. In this specific case, the condition part of line 1 is not true because the robot is not close to the ball. For the same reason, the condition part of line 2 is not true either. However, the condition part of line 3 is true, Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 421
- 422. 25 Controversies and Alternative Paradigms so the robot activates the associated primitive action move-forward. In the meantime and while the move-forward action is proceeding (this is important!), the part of the robot system that is checking to see which is the first true line in the program is still checking (in the background, as it were). Sooner or later (if we assume the robot does not change its “heading” while it is moving forward), the condition part of line 1 will become true. Precisely at that time, line 3 is no longer the first line of the program whose condition part is true; line 1 is. So, line 3’s action part is suspended, line 1’s action part is activated, and the ball is kicked away. Now, to illustrate the robustness of T-R programs and to explain how subprograms are activated, let’s assume everything is the same as before (namely, the robot is facing the ball and is far away from the ball) but that, during the time that the robot is moving forward (because move-forward is being activated), the robot inadvertently drifts off course so that it is no longer facing the ball. At the time the robot perceives this change, line 3 of the program is no longer the first line whose condition part is true – line 4 is (because its condition, namely, True is assumed always to be true.) So at that time activation of move-forward ceases, and instead moveto(Ball) is activated. To activate moveto(Ball), the program moveto(x) is retrieved from the “program library,” and its parameter, x, is replaced by Ball, and the following program is activated: moveto(Ball): 1. Close(Ball) -> do-nothing 2. Facing(Ball) -> move-forward 3. True -> face(Ball) The first line of this program whose condition part is true is line 3 – resulting in activating face(Ball), another subprogram. If we assume that the robot’s drift off its heading resulted in the ball being to its left, activation of face(Ball) will cause the robot to rotate in a counterclockwise direction. Sooner or later, the robot will be facing the ball again. Now, an interesting thing happens. The subprogram moveto(Ball), with all of its condition-checking apparatus, is still running in the background. Its line 2 is now the first line in the program whose condition part is true (instead of its line 3 as before). So, the face(Ball) program ceases operation and the move-forward primitive program is activated. If nothing further untoward happens, line 1 of kick(x) will be the first line in that program whose condition part is true [moveto(Ball) will be suspended], and foot-swing will be activated. (Whew! It’s easier for the circuitry that controls all of this to function automatically than it is for us to think about it.) If you aren’t exhausted, you might want to consider some of the other ways that these programs might be activated. 422 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 423. 25.5 Brain-Style Computation 25.5 Brain-Style Computation 25.5.1 Neural Networks Because apparently the brain does what it does by massive parallel computations implemented by networks of interconnected neurons, some people began anew to explore the possibilities of neural networks. During the late 1970s a group at the University of California at San Diego (UCSD) headed by cognitive psychologists David E. Rumelhart (1942– ) and James L. McClelland (1948– ) (Fig. 25.3) began a study of networks that they called “parallel distributed processing” (PDP) systems. The group came to be known as the PDP group. Figure 25.3: David Rumelhart (left) and James McClelland (right). (Rumelhart photograph courtesy of Donald Rumelhart. McClelland photograph courtesy of James McClelland.) The PDP group held that mental processes in the brain were the result of interactions among elementary neural units connected in networks. These units excite and inhibit each other in parallel. This view of computation is quite at odds with the serial computations performed by most symbol processing approaches and with Newell and Simon’s physical symbol system hypothesis. Thus, rather than storing information as lists in localized data structures, PDP systems distributed information throughout the connections among the Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 423
- 424. 25 Controversies and Alternative Paradigms units. Furthermore, PDP neural networks were not limited to feed-forward, layered arrangements. Harking back to Rosenblatt’s original view of general perceptrons, some of the PDP systems allowed what were called “recurrent” connections – ones that were parts of loops through the various units. As Rumelhart later pointed out, “The common theme to all these efforts has been an interest in looking at the brain as a model of a parallel computational device very different from that of a traditional serial computer.”17 PDP work gained prominence with the publication of two volumes by McClelland, Rumelhart, and the PDP Research Group.18 An important chapter in Volume One, titled “Learning Internal Representations by Error Propagation” by Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams, introduced a new technique, called “back propagation,” for adjusting network weights. It led to many new applications, which I’ll describe in a subsequent chapter. The physicist John J. Hopfield (1933– ) invented another type of neural network.19 Each neural element in a Hopfield network is connected to all of the others. The weights on these connections are symmetrical; that is, the weight connecting unit i to unit j has the same value as the weight connecting unit j to unit i. The operation of the network is a dynamical process; that is, the values of the units at each time step depend on the values at the just-preceding time step. The collection of unit values are related to what physicists call an “energy function,” and (regardless of the initial state of the network) these values tend to converge to values that correspond to a locally minimal energy state. These are called the “stable states” of the network and can be thought of as the set of memories stored by the net. Hopfield nets have been used as associative memories and for some simple computations. (For a demonstration of a Hopfield net solving a ten-city “traveling salesman” problem, visit http://to-campos.planetaclix.pt/neural/hope.html.) A “Boltzmann machine” is an elaboration of the Hopfield net in which unit values at each time step depend randomly on the unit values at the just-preceding time step. Much of the neural network research during this period came to be called “connectionist” or “brain-style” computation, to contrast it with GOFAI. Another person active in this movement was Jerome A. Feldman, who in 1974 moved from Stanford to the University of Rochester to set up the Department of Computer Science there as well as to pursue connectionist-oriented research.20 25.5.2 Dynamical Processes Some researchers believe that dynamical processes, similar to those exhibited by Hopfield and Boltzmann networks (and including those described by sets of differential or difference equations), underlie much of the computation performed by the brain. For example, in an article in The MIT Encyclopedia of Cognitive Science, Tim van Gelder wrote21 424 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 425. 25.6 Simulating Evolution A dynamical system for current purposes is a set of quantitative variables changing continually, concurrently, and interdependently over quantitative time in accordance with dynamical laws described by some set of equations. Hand in hand with this first commitment goes the belief that dynamics provides the right tools for understanding cognitive processes. . . . A central insight of dynamical systems theory is that behavior can be understood geometrically, that is, as a matter of position and change of position in a space of possible overall states of the system. The behavior can then be described in terms of attractors, transients, stability, coupling, bifurcations, chaos, and so forth – features largely invisible from a classical perspective. However, in the same article van Gelder wrote “Currently, many aspects of cognition – e.g., story comprehension – are well beyond the reach of dynamical treatment.” The University of Indiana computer scientist Randall Beer (1961– ) is more optimistic. In an article titled “Dynamical Approaches to Cognitive Science,” Beer wrote that “dynamical approaches are beginning to engage substantive empirical questions in cognitive science.”22 He gives three examples, one of which is a simulated agent whose horizontal motion is controlled by a dynamical system implemented by a fourteen-neuron, continuous-time recurrent neural network. The agent’s task is to discriminate between two differently shaped falling objects – avoiding one shape (by moving out of its path) and engaging the other (by moving into its path). He terms this behavior “minimally cognitive,” which he defines as “the simplest behavior that begins to raise questions of cognitive interest.” To my knowledge, dynamical systems have not yet been used in tasks requiring more than these minimally cognitive behaviors. A feature stressed by Beer and others is the importance of the interaction between the network and its environment. Indeed, the environment itself provides an important component of most dynamical systems. Exploiting properties of the environment to make the overall system simpler has been carried to an extreme by Mark W. Tilden, who did some of his work at the Los Alamos National Laboratory. For example, Tilden’s walking robots don’t use computers at all but are able to walk by exploiting the resistive input from their motors as they amble over rough terrain.23 25.6 Simulating Evolution In Chapter 2, I discussed attempts to create intelligent artifacts by using the evolutionary processes of random generation and selective survival. Of these, Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 425
- 426. 25 Controversies and Alternative Paradigms John Holland’s genetic algorithms (GAs) seemed to offer the most promise. GAs attempt to evolve strings of symbols that encode a solution to some particular problem. Much of the early work in GA used binary-valued symbols (0’s and 1’s), although other symbols can be used also. The traveling salesman problem is often used to illustrate the use of GAs. In that problem, we have a list of cities that must be visited, and we must find a tour that starts at one city, visits all of the others just once, and returns to the starting city. The problem is to find an ordering of the cities that minimizes the total distance traveled. To encode the solution, the names of the cities can be used as the symbols. If, for example, there are fourteen cities named by the letters A, B,. . . , N, and if we must start and end at city C, then the symbol string (C, F, N, K, B, L, M, H, D, A, E, G, I, J, C) would represent a tour that starts with C, visits F next, and so on. In keeping with evolutionary terminology, the total distance traversed by this tour is related to the fitness of this string. We want shorter tours to have greater fitness, so let us set the fitness of a tour to minus its distance traveled. The GA process attempts to evolve a a string having maximal fitness. The evolutionary process starts by assembling a large population of random strings. In our traveling-salesman-problem example, they would all start and end with C but have all of the other names just once in each string. Populations of these strings are subjected to two different processes – analogous to some of what happens in biological evolution. First, some of the strings undergo random mutations in which the values of some of their components are changed. An example of a mutation of a traveling-salesman string might be to interchange two symbols selected randomly within the string. Second, pairs of strings within a population that have relatively high fitness are selected to participate in an operation called “cross-over,” which generates an “offspring” string. Different kinds of cross-over operations are used in GAs. For the traveling-salesman example, the operation must preserve the “legality” of the offspring string; that is, it must correspond to a tour that visits the other cities just once. One way to do this is to repeat in the offspring string the first k symbols of one of the parents and then scan the symbols in the other parent to fill out the offspring string with symbols not already appearing there. The value of k is selected randomly. The illustration in Fig. 25.4 shows how this style of cross-over operates. At each stage of the evolutionary process, the current generation of strings gives rise to a new generation. The new generation contains some of the strings from the old one (preferring strings with high fitness), the mutated strings, and the new strings resulting from cross-over operations. Interestingly, succeeding generations eventually contain strings that are better and better at solving the problem at hand (for some kinds of problems). GAs have been applied to various combinatorial optimization problems in 426 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 427. 25.6 Simulating Evolution Figure 25.4: A cross-over operation. computer science, engineering, economics, chemistry, and other fields. The online encyclopedia Wikipedia has some excellent material on GAs, including pointers to tutorials, at http://en.wikipedia.org/wiki/Genetic algorithm.24 We can think of a genetic algorithm as a search process attempting to locate high points in a “fitness landscape.” Each possible GA string can be thought of as a “place” in a contour map, with the fitness of that string being the elevation at that place. Initially, “paratroopers” are dropped randomly over the landscape, and these report their elevations. Some of them move slight distances from their current positions (corresponding to the mutations), and some pairs rocket to a position somewhere in between their current positions (corresponding to cross-over). Then the process repeats. The fitness landscape may have several peaks, with some higher than others, and it may have several plateaus. After several generations, a GA process may succeed only in finding the location of minor peaks, or it may have difficulty getting off a large plateau. But occasionally it may find the highest peak in the landscape. AI has used what mathematicians call hill-climbing (or gradient ascent) procedures, but before GAs these techniques usually involved only one “climber.” GAs, along with other evolutionary algorithms, allow several climbers to search simultaneously, resulting in what is called “parallel search.” One of John Holland’s students, John Koza (1943– ; Fig. 25.5), developed a somewhat different evolutionary procedure called Genetic Programming (GP). GP evolves LISP programs rather than strings. The process starts with a random collection of programs containing some basic LISP functions and constants thought to be important for solving the task at hand. Again, random mutations and cross-over are used to produce new generations of programs. (Later versions of GP have added biologically inspired operations analogous to inversion, gene duplication, and gene deletion.) Various techniques can be used for the mutation part, including replacing parts of a Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 427
- 428. 25 Controversies and Alternative Paradigms program with randomly selected new program components. In cross-over, two “parent programs” with relatively high fitness are selected. Randomly selected parts of each program are then interchanged to produce two new programs for the next generation of programs. Figure 25.5: John Koza. (Photograph courtesy of John Koza.) Koza has employed GP to produce programs that have created new kinds of electrical filters, optical lenses, antennas, and control circuitry, among other things. Many of these programs are, as he says, “competitive with human performance.” He claims that because the main goal of AI is to produce programs that are capable of intelligent behavior, one should use a program synthesis technique able to produce such programs directly and that GP is (so far) the best such synthesis technique. As he puts it, “Virtually all problems in artificial intelligence, machine learning, adaptive systems, and automated learning can be recast as a search for a computer program. Genetic programming provides a way to successfully conduct the search for a computer program in the space of computer programs.”25 Since 1999, Koza has been using a 1,000-Pentium “Beowulf-Style Cluster Computer” for his GP work.26 He and co-authors have written several books and papers on GP.27 There is a Special Interest Group for Genetic and Evolutionary Computation (SIGEVO) of the Association for Computing Machinery (ACM). It sponsors conferences dealing with various aspects of evolutionary 428 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 429. 25.7 Scaling Back AI’s Goals computation including GAs and GPs.28 A compelling narrated video demonstration of the power of simulated evolution, presented at the 1991 SIGGRAPH conference, can be viewed at http://www.archive.org/details/sims evolved virtual creatures 1994. As described by the Web site, “A population of several hundred creatures is created within a supercomputer, and each creature is tested for [its] ability to perform a given task, such [as] the ability to swim in a simulated water environment.”29 25.7 Scaling Back AI’s Goals During the AI winter, many AI researchers began to focus on more modest and achievable goals than on those of previous years. One heard fewer brave predictions about what AI could ultimately achieve. Increasingly, effort was devoted to what AI could (at the time) actually achieve. The result was more work on limited or “weak” AI and less on “strong AI.” The emphasis was on using AI to help humans rather than to replace them. Companies and government agencies with funds to support research looked to computer technologies generally (rather than to AI specifically) to help solve their problems. Research funds were directed at improving database systems, user interfaces, graphics, computer networks, data mining, computer games, information retrieval, computer vision, and word-processing and spreadsheet programs, to name just a few areas. The AI technologies of search and inference, expert systems, speech recognition, and natural language processing were used, when appropriate, as components of large, integrated systems. AI researchers began to be satisfied with adding bits of intelligence here and there to these systems to make them more useful and appealing. Notes 1. Michael Genesereth and Nils Nilsson, Logical Foundations of Artificial Intelligence, San Francisco: Morgan Kaufmann Publishers, 1987. [413] 2. Drew McDermott, “A Critique of Pure Reason,” Computational Intelligence, Vol. 3, No. 3, pp. 151–160, August 1987. [413] 3. For a brief online summary with citations to longer articles, see Glenn Shafer, “Dempster–Shafer Theory,” 2002; available at http://www.glennshafer.com/assets/downloads/articles/article48.pdf. [414] 4. See, for example, David L. Hall and Sonya A. H. McMullen, Mathematical Techniques in Multisensor Data Fusion, Norwood, MA: Artech House, Inc., 2004. [414] 5. Zadeh’s original article is Lotfi Zadeh, “Fuzzy Sets,” Information and Control, Vol. 8, pp. 338–353, 1965; available online at http://www-bisc.cs.berkeley.edu/Zadeh-1965.pdf. For a summary, see E. Cox, “Fuzzy Fundamentals,” IEEE Spectrum, Vol. 29, No. 10, pp. 58–61, 1992. The Association for the Advancement of Artificial Intelligence (AAAI) maintains a Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 429
- 430. 25 NOTES fuzzy logic Web page (with lots of pointers to tutorials, papers, and applications) at http://www.aaai.org/AITopics/html/fuzzy.html. [414] 6. See, for example, Kevin M. Passino and Stephen Yurkovich, Fuzzy Control, Menlo Park, CA: Addison Wesley Longman, 1998. (The book is no longer in print but can be downloaded from http://www.ece.osu.edu/∼passino/FCbook.pdf.) [415] 7. See Machine Design, March 1995. [415] 8. See The Berkeley Initiative in Soft Computing Web site at http://www-bisc.cs.berkeley.edu/. [416] 9. Marvin Minsky, “Smart Machines,” Chapter 8 of John Brockman, The Third Culture: Beyond The Scientific Revolution, New York: Simon & Schuster, 1995. Available online at http://edge.org/documents/ThirdCulture/p-Ch.8.html. The whole book is available at http://www.edge.org/documents/ThirdCulture/d-Contents.html. [416] 10. The quotation is taken from Wendy G. Lehnert, “Cognition, Computers, and Car Bombs: How Yale Prepared Me for the 1990s,” in Roger Schank and Ellen Langer (eds.), Beliefs, Reasoning, and Decision Making: Psycho-Logic in Honor of Bob Abelson, pp. 143–173, Hillsdale, NJ: Lawrence Erlbaum Associates, 1994. [417] 11. Rodney A. Brooks, “From Earwigs to Humans,” Proceedings IIAS The Third Brain and Mind International Symposium on Concept Formation, Thinking and Their Development, pp. 59–66, Kyoto, Japan, May 1996. Available online at http://people.csail.mit.edu/brooks/papers/ascona.pdf. [417] 12. For a description of how Genghis works, see Rodney A. Brooks, “A Robot That Walks: Emergent Behavior from a Carefully Evolved Network,” Neural Computation, Vol. 1, No. 2, pp. 253–262, Summer 1989. Also in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 292–296, Scottsdale, AZ, May 1989. Available online as an MIT AI Lab Memo (No. 1091) at http://people.csail.mit.edu/brooks/papers/AIM-1091.pdf. [419] 13. Rodney A. Brooks, “Elephants Don’t Play Chess,” Robotics and Autonomous Systems, Vol. 6, pp. 3–15, 1990. Also in Pattie Maes (ed), Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back, pp. 3–15, Cambridge, MA: MIT Press, 1990. Available online at http://people.csail.mit.edu/brooks/papers/elephants.pdf. [419] 14. Rodney A. Brooks, op. cit. [419] 15. Rodney A. Brooks, op. cit. [419] 16. See Brooks’s Web pages for pointers to others of his publications: http://people.csail.mit.edu/brooks/. [419] 17. David E. Rumelhart, “Brain Style Computation: Learning and Generalization,” in Steven E Zornetzer, Joel L. Davis, and Clifford Lau (eds.), An Introduction to Neural and Electronic Networks, San Diego: Academic Press, 1990. [424] 18. James L. McClelland, David E. Rumelhart, and the PDP Research Group, Parallel Distributed Processing, Explorations in the Microstructure of Cognition, Volume 1: Foundations, Cambridge, MA: MIT Press, 1986, and James L. McClelland, David E. Rumelhart, and the PDP Research Group, Parallel Distributed Processing, Explorations in the Microstructure of Cognition, Vol. 2: Psychological and Biological Models, Cambridge, MA: MIT Press, 1986. [424] 19. John J. Hopfield, “Neural Networks and Physical Systems with Emergent Collective Computational Abilities,” Proceedings of the National Academy of Science, Vol. 79, No. 8, pp. 2554–2558, 1982. Available online from http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=346238. [424] 20. See, for example, Jerome A. Feldman et al., “Computing with Structured Connectionist Networks,” Communications of the ACM, Vol. 31, No. 2, pp. 170–187, February 1988. [424] 430 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 431. 25.7 NOTES 21. T. J. van Gelder, “Dynamic Approaches to Cognition” in R. Wilson and F. Keil (eds.), The MIT Encyclopedia of Cognitive Sciences, pp. 244–246, Cambridge MA: MIT Press, 1999. Available online at http://sites.google.com/site/timvangelder/publications-1/ dynamic-approaches-to-cognition/MITDyn.pdf?attredirects=0. [424] 22. Randall D. Beer,“Dynamical Approaches to Cognitive Science,” Trends in Cognitive Sciences, Vol. 4, No. 3, March 2000; available online at http://mypage.iu.edu/∼rdbeer/Papers/TICS.pdf. For a longer paper, see Randall D. Beer, “A Dynamical Systems Perspective on Agent-Environment Interaction,” Artificial Intelligence, Special Volume on Computational Research on Interaction and Agency, Part 1, Vol. 72, Nos. 1–2, pp. 173–215, 1995; available online at http://mypage.iu.edu/∼rdbeer/Papers/AIJ95.pdf. Beer’s Web pages (http://mypage.iu.edu/∼rdbeer/) provide many additional citations. [425] 23. For more about these kinds of simple robots, visit the Wikipedia site http://en.wikipedia.org/wiki/BEAM robotics. [425] 24. See also Melanie Mitchell, An Introduction to Genetic Algorithms, Cambridge, MA: MIT Press, 1996. [427] 25. The quotation is from Koza’s homepage at http://www.genetic-programming.com/johnkoza.html. [428] 26. See Forest H. Bennett et al., “Building a Parallel Computer System for $18,000 That Performs a Half Peta-flop per Day,” in Wolfgang Banzhaf et al. (eds.), GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1484–1490, San Francisco, CA: Morgan Kaufmann Publishers, 1999. [428] 27. See, for example, John R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, Cambridge, MA: MIT Press, 1992; John R. Koza, Genetic Programming II: Automatic Discovery of Reusable Programs, Cambridge, MA: MIT Press, 1994; John R. Koza, Forrest H Bennett III, David Andre, and Martin A. Keane, Genetic Programming III: Darwinian Invention and Problem Solving, San Francisco: Morgan Kaufmann Publishers, 1999; and John R. Koza, Martin A. Keane, Matthew J. Streeter, William Mydlowec, Jessen Yu, and Guido Lanza, Genetic Programming IV: Routine Human-Competitive Machine Intelligence, Norwell, MA: Kluwer Academic Publishers, 2003. [428] 28. Visit the SIGEVO Web site at http://www.sigevo.org/index.html. [429] 29. I thank Mykel Kochenderfer for telling me about this video. [429] Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 431
- 432. 25 NOTES 432 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 433. 25.7 Part VII The Growing Armamentarium: From the 1980s Onward Throughout the 1980s, while AI was enjoying increased popularity and commercial successes and then suffering funding cuts and a wintry season, its basic research workers produced a significant number of powerful new technical tools and sharpened others. New results unfolded in all of its subfields, including reasoning and representation, machine learning, natural language processing, and computer vision. This work, technically and mathematically deeper than before and strengthened by new connections with statistics and control engineering, helped vitiate some of the criticisms hurled at earlier AI systems and greatly enhanced AI’s abilities. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 433
- 434. 25 434 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 435. 26.1 Chapter 26 Reasoning and Representation 26.1 Nonmonotonic or Defeasible Reasoning Those AI researchers called logicists, who favor the use of logical languages for representing knowledge and the use of logical methods for reasoning, acknowledge one problem with ordinary logic; namely, it is monotonic. By that they mean that the set of logical conclusions that can be drawn from a set of logical statements does not decrease as more statements are added to the set. If one could prove a statement from a given knowledge base, one could still prove that same statement (with the very same proof!) when more knowledge is added. Yet, much human reasoning does not seem to work that way – a fact well noticed (and celebrated) by AI’s critics. Often, we jump to a conclusion using the facts we happen to have, together with reasonable assumptions, and then have to retract that conclusion when we learn some new fact that contradicts the assumptions. That style of reasoning is called nonmonotonic or defeasible (meaning “capable of being made or declared null and void”) because new facts might require taking back something concluded before. One can even find examples of nonmonotonic reasoning in children’s stories. In That’s Good! That’s Bad!, by Margery Cuyler,1 a little boy floats high into the sky holding on to a balloon his parents bought him at the zoo. “Wow! Oh, that’s good,” the story goes. The balloon breaks on a branch of a tall, prickly tree. “Pop! Oh, that’s bad,” the story continues. The boy falls into a muddy river and climbs up onto a hippopotamus and rides to shore. “Oh, that’s good.” The story goes on like that – changing back and forth about whether the balloon ride is turning out “good” or “bad.” Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 435
- 436. 26 Reasoning and Representation There were already some methods used in AI (and elsewhere in computer science) for defeasible reasoning. For example, in the problem-solving language PLANNER proposed by Carl Hewitt, if a goal, say G, could not be achieved by a program then Not G could be asserted (under the assumption that G was a statement that the program was trying to establish). Such reasoning was defeasible because if additional statements were later added to the program or to its knowledge base, then establishing G might become possible. Similarly, in the PROLOG programming language, if a statement could not be proved by a program, then it was inferred to be false. Inferring that something is false if it cannot be proved true is called “negation as failure.” The SRI planning system, STRIPS, was also a type of nonmonotonic reasoning system. Assumptions about things “staying the same” after actions were performed were certainly just that – assumptions. Conclusions drawn after making such assumptions might be defeated after adding new information whose implications might negate those assumptions. Another method for defeasible reasoning was being used in the database world. Databases are used for encoding a wide variety of information. For example, a company might have a database about its employees. One can query such a database to find out an employee’s salary, the department he or she works in, and so on. Suppose we attempt to find out from one of these employee databases information about a person, say Jack Smith, whose name is not found in the database. We might reasonably conclude then that Mr. Smith is not an employee of that company, and that’s what some database systems would do. That conclusion would be based on the assumption that the database names all of that company’s employees – an instance of the so-called closed-world assumption (CWA). Of course, Jack Smith may later join the company, and then his name would be added to the database. At that time we would have to take back the conclusion that Jack Smith is not one of the company’s employees; this is another example of defeasible reasoning. You may recall that, way back in 1964, Bertram Raphael’s question-answering system, SIR, included a style of defeasible reasoning he called the “exception principle.” In SIR, general information about all the elements of a set applied to particular elements – but only in the absence of more specific information about those particular elements. Several AI knowledge representation schemes represent some of their knowledge in “taxonomic hierarchies,” somewhat like the one Raphael used, and use the exception principle, which is now often called “cancellation of inheritance,” for defeasible reasoning. In Fig. 26.1, I show a taxonomic hierarchy of some office machines. A program using this hierarchy would conclude that the energy source of a laser printer, for example, is a wall outlet because that property is inherited from the general class “office machines.” However, more specific information about the energy source for robots would force the conclusion that the energy source for R2D2, for example, is a battery, overriding the inheritance of properties of 436 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 437. 26.1 Nonmonotonic or Defeasible Reasoning Figure 26.1: A hierarchy of office machines. (From Nils J. Nilsson, Artificial Intelligence: A New Synthesis, p. 311, San Francisco: Morgan Kaufmann Pub- lishers, 1998.) the general class of office machines. During the 1980s some of the most creative AI researchers became fascinated with the problem of defeasible reasoning and made several new proposals for how to do it. Their proposals were accompanied by a good deal of theoretical analyses comparing and contrasting the different approaches and how some of them could be considered either as specializations or as generalizations of the others. The Canadian AI researcher Raymond Reiter (1939–2002) proposed one of the new methods.2 In its simplest form, it uses special inference rules that permit drawing a conclusion from a knowledge base if some specified condition is satisfied and if that conclusion is not contradicted by what could ordinarily be deduced from that knowledge base. Reiter’s special inference rules are called default rules, and his system that uses them is called default logic. As an example of its use, suppose we have a knowledge base used by a robot that specifies which rooms in an office building may be entered by the robot. We might have a rule that says that for rooms on the second floor, if it is impossible to prove that the robot may not enter a room there, then one can conclude that the robot may enter that room. Again, the reasoning is Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 437
- 438. 26 Reasoning and Representation defeasible, because someone may later add to the knowledge base a fact that permits the deduction that some room on the second floor may not be entered. John McCarthy, the originator of the proposal that knowledge should be encoded as logical statements, was also worried about the problem of nonmonotonicity. To deal with the problem, he proposed a method called circumscription.3 Circumscription is rather difficult to explain without using a lot of logical jargon. In principle, a version of it called “predicate circumscription” (just one of several versions of circumscription4 ) involves limiting (thus “circumscribing”) the set of entities that make predicates true to just those that can be proved to be true. For example, if we have a knowledge base that contains statements such as Tall(John) and Tall(Frank), plus a lot of other facts, we can circumscribe (if we wish) the predicate Tall. Doing so allows us to conclude ¬Tall(Susy) if Tall(Susy) is not logically implied by the knowledge base. One of the motivations for McCarthy’s interest in nonmonotonic reasoning was the possibility that it would be a key to solving the frame problem (see p. 222). Recall that the frame problem concerns the difficulty of how to represent which things change and which things stay the same when an action is taken (say by a robot). One approach is to make the assumption that if a predicate describing some state of the world is not mentioned by a description of an action (including the action’s preconditions and effects) then that predicate is not changed by the action. This assumption is nonmonotonic because later (or more detailed) information may imply that a nonmentioned predicate is indeed changed. Some early attempts to solve the frame problem using nonmonotonic reasoning ran into various technical difficulties (which are too technical to bother about here), but work continued. A recent paper claimed that “the Frame Problem as it was originally formulated has been solved with Shanahan’s and Thielscher’s approaches and that at least the logical chapter of the Frame Problem has been closed.” (The two people mentioned are Murray Shanahan of Imperial College, London, and Michael Thielscher of the Dresden University of Technology.)5 It might not have escaped your attention that the many proposals for nonmonotonic reasoning are rather similar, but there are many subtle technical differences. There are even other proposals that I have not mentioned, including auto-epistemic logics, nonmonotonic logics, abductive reasoning, truth-maintenance systems, and methods based on probability theory.6 This profusion of defeasible reasoning methods can probably be attributed to the creativity and mathematical sophistication of many of the AI researchers involved and their keen abilities to spot and to attempt to escape the limitations of each others’ proposals. 438 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 439. 26.2 Qualitative Reasoning 26.2 Qualitative Reasoning Many of you have probably taken courses in physics, either in high school or in college or in both. The job of physics is to build theories of the physical world, and these theories are usually formulated using mathematics. For example, the formula F = Ma, relates the force, F, acting on an object to the object’s mass, M, and its acceleration, a. Many mathematical formulas describing physical processes are more complex. For example, the following “wave equation” can be used to calculate the velocity of a water wave: v = s gλ 2π tanh 2π h λ . Engineers could use it, for example, to predict when the crest of a wave would pass by a certain point. We humans are also able to predict, with useful accuracies, the future course of many of the physical processes we commonly experience. For example, when people play in ocean waves at the beach, they are usually able to predict when a wave crest will arrive so that they can jump up in time. Do our brains use anything like the equation just shown to make that prediction? Probably not. Instead, prediction routines for guiding skilled actions are learned by repeated experiences and are part of what psychologists call “procedural knowledge.” In addition to acting automatically and effectively using procedural knowledge embedded in our various motor skills, we can also make declarative statements predicting what will happen in certain situations. For example, a surfer looking out at incoming waves can tell a friend “Take the next wave; it’s going to be a big one.”7 Apparently we have some facility for representing and using “qualitative knowledge” about physical processes – knowledge that is neither part of our procedural “muscle memory” nor represented in our brains by complex mathematical formulas. I could give several examples. How do we know that when we knock over a glass of water on a table the water will eventually come to the edge of the table and spill off? How do we know that if we stack heavy boxes on top of light, fragile boxes, the fragile boxes might collapse? How do we know that if we drive to our destination a little bit faster, we’ll get there a little bit sooner? Several AI researchers have worked on systems having the ability to represent and to reason with qualitative knowledge. Scott Fahlman (1948– ) wrote such a program for his master’s thesis while a student at MIT. Called BUILD, the program was able to plan how to stack toy blocks by taking into account various forces acting on the blocks, such as gravity and friction.8 It did this in a more-or-less qualitative way rather than by using exact mathematical models. Thus, BUILD can be said to be one of the first AI attempts to do qualitative reasoning about physics. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 439
- 440. 26 Reasoning and Representation Soon after, another MIT student, Johan de Kleer (1951– ), wrote a program called NEWTON for his master’s thesis that included a component able to do qualitative reasoning. NEWTON, de Kleer claimed, “understands and solves problems in a mechanics mini-world of objects moving on surfaces.”9 NEWTON used its qualitative knowledge about physics to produce approximate problem solutions, which it then used to plan and carry out subsequent quantitative calculations. Most physics teachers will tell you that qualitative reasoning about “the physics” of a problem is essential before plunging into the mathematics. In doing qualitative reasoning, NEWTON used a process called “envisioning” for “generating a progression of scenes encoded in a symbolic description which describe what could happen.” It used descriptions of six basic actions appropriate to the kinds of problems NEWTON could solve. One of these descriptions, for example, was for FLY. It encoded the knowledge that “[i]f the object is moving on top of a surface which is concave away from the motion, the object might fly off.” de Kleer’s main contribution was to show how qualitative calculations and quantitative reasoning can be combined in a computer program. In 1979 Pat Hayes published “The Naive Physics Manifesto.”10 A revised version appeared in 1985.11 He proposed that the artificial intelligence research community begin “the construction of a formalization of a sizable portion of common-sense knowledge about the everyday physical world: about objects, shape, space, movement, substances (solids and liquids), time, etc.” These topics had long presented particularly difficult representational and reasoning challenges for AI. Encoding our everyday knowledge about these subjects so that computers can reason about them is at the heart of qualitative physics, which Hayes called “naive physics.” His manifestos presented some general ideas about how to represent “clusters” of common-sense knowledge about the physical world. As one example, he proposed the notion of “histories” for representing events, instead of states and functions of states as he and John McCarthy had earlier advocated. He defined a history as “a piece of spacetime with natural boundaries, both temporal and spatial.” For example, “the event of putting four blocks together in a square is the beginning of the history of a platform, and the end of that history is when and where they are separated from one another.” Hayes said, in effect, that we should not be “too hasty” about writing naive physics programs – preferring instead to delay implementations until more foundational work had been done on the representations themselves. He had already sketched out some of this work on liquids.12 These initial explorations in qualitative reasoning soon led to a rapidly growing subfield of AI with many applications, especially in diagnosing faults based on qualitative models of equipment. (I’ll mention one example, 440 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 441. 26.3 Semantic Networks diagnosing faults in spacecraft equipment, in Part 8.) Prominent groups were formed by Professor Kenneth D. Forbus (1955– ) at Northwestern University13 and by Benjamin Kuipers (1949– ) at the University of Texas at Austin. (Kuipers has now relocated to the University of Michigan).14 Special issues of journals and edited volumes and books devoted to the subject have appeared.15 I’ll conclude this chapter on Reasoning and Representation by turning next to new developments in the use of semantic networks for knowledge representation. 26.3 Semantic Networks In my earlier discussion of defeasible reasoning, I showed a semantic network representing a taxonomic hierarchy of office machines. Taxonomic networks are widely used in AI and in computer science to represent what are called “ontologies.” In AI, an ontology consists of a set of concepts and relationships among those concepts. (In philosophy, it means the study of being or existence.) AI systems for reasoning with these networks would commonly have mechanisms for property inheritance using exception principles. Although we understand taxonomic networks best by thinking about them in the form of trees, a collection of special data structures is used when encoding them for computers. These structures are often called “frames,” following Minsky’s original use of the word. For example, one of the frames for the office machines network might be represented as in Fig. 26.2. Typically there would be a frame for each class of individuals or entities in a taxonomy as well as for each of the entities themselves. Frames for classes would name the superclass to which it belonged and the subclasses belonging to it. It would also specify properties of the entities belonging to the class. It is also common for a frame to have “meta-information,” such as the date the frame was created. 26.3.1 Description Logics Earlier in AI’s history there was controversy about whether knowledge should be represented by data structures such as semantic networks (encoded, say, as frames) or by sets of logical statements. Gradually the controversy moderated because researchers came to accept the idea that semantic networks could be thought of as a special way of representing certain kinds of logical statements, thereby permitting some deductions to be made directly from the network. Two of the researchers who helped to establish this view were Ronald J. Brachman (1949– ) and Hector J. Levesque (1951– ) (Fig. 26.3). (Each of them has also done related foundational work in knowledge representation and Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 441
- 442. 26 Reasoning and Representation Figure 26.2: A frame. (Adapted from Nils J. Nilsson, Artificial Intelligence: A New Synthesis, p. 313, San Francisco: Morgan Kaufmann Publishers, 1998.) reasoning generally.16 ) Figure 26.3: Ronald Brachman (left) and Hector Levesque (right). (Courtesy of Ronald Brachman and of Hector Levesque.) Brachman did his Ph.D. work17 at Harvard under Bill Woods. (Besides his work in natural language processing, Woods had also written about the 442 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 443. 26.3 Semantic Networks relationship between semantic networks and logic.18 ) Expanding on the ideas in his thesis, Brachman, along with other colleagues at BBN (including Woods) and at USC-ISI, developed a frame-based knowledge representation system called KL-ONE,19 which became the basis for what came to be called description logics. Levesque did all of his college work (B.S., M.S., and Ph.D.) at the University of Toronto. After receiving his Ph.D. degree in 1981, he joined Brachman at the Fairchild Laboratory for Artificial Intelligence Research in Palo Alto, a group founded by Peter Hart after Hart left SRI. There, Brachman and Levesque, together with Richard Fikes (then at Xerox PARC), developed the KRYPTON representation and reasoning system.20 KRYPTON was a hybrid system – meaning it represented knowledge both by logical formulas and by a semantic network. Although semantic networks make it easy to reason about individuals and their properties in a hierarchy, it is difficult for them to represent statements containing negations and disjunctions. As the KRYPTON paper states, “. . . a statement such as ‘either Elsie or Bessie is the cow standing in Farmer Jones’s field’ cannot be made in a typical assertional frame system.” KRYPTON’s solution is to use a combination of both types of representations: . . . we have split the [reasoning] operations into two separate kinds, yielding two main components for our representation system: a terminological [that is, network-based] one, or T Box, and an assertional [that is, logic-based] one, or A Box. The T Box allows us to establish taxonomies of structured terms and answer questions about analytical relationships among these terms; the A Box allows us to build descriptive theories of domains of interest and to answer questions about those domains.21 The diagram from the KRYPTON paper shown in Fig. 26.4 illustrates the structure of the system. As the authors wrote, it consists of “a T Box of structured terms organized taxonomically, an A Box of (roughly) first-order sentences whose predicates come from the T Box, and a symbol table maintaining the names of the T Box terms so that a user can refer to them.” The T Box in the diagram represents (among other things) that a child is a person. The A Box states that there exists a doctor who has a child. KRYPTON was the forerunner of several description-logic systems, including CLASSIC, developed by Brachman and colleagues after he moved to ATT Bell Laboratories.22 In addition to their use in AI reasoning systems, description logics are used in ontology languages for the semantic Web, for example DAML-ONT23 and OWL.24 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 443
- 444. 26 Reasoning and Representation Figure 26.4: Parts of a KRYPTON T Box and A Box. (Adapted from Ronald J. Brachman, Richard E. Fikes, and Hector J. Levesque, “KRYPTON: A Functional Approach to Knowledge Representation, IEEE Computer, Vol. 16, No. 10, p. 71, October 1983.) 26.3.2 WordNet WordNet is a large “conceptual” dictionary of English words, organized somewhat like a semantic network and inspired by psycholinguistic and computational theories of human lexical memory.25 Its development was begun at Princeton University in the 1980s under the direction of Professor George A. Miller (the same George Miller whom I mentioned earlier and who wrote “The Magical Number Seven, Plus or Minus Two”). In a 1990 paper, Miller and his colleagues had this to say about the beginnings of the project:26 In 1985 a group of psychologists and linguists at Princeton University undertook to develop a lexical database along lines suggested by [earlier psycholinguistic] investigations. The initial idea was to provide an aid to use in searching dictionaries conceptually, rather than merely alphabetically – it was to be used in close conjunction with an on-line dictionary of the conventional 444 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 445. 26.3 Semantic Networks type. As the work proceeded, however, it demanded a more ambitious formulation of its own principles and goals. WordNet is the result. Inasmuch as it instantiates hypotheses based on results of psycholinguistic research, WordNet can be said to be a dictionary based on psycholinguistic principles. WordNet groups its words into collections called “synsets.” Each synset contains a group of synonymous words, that is, words with more-or-less the same meaning. I’ll use WordNet’s online search facility (at http://wordnetweb.princeton.edu/perl/webwn) to provide some examples of synsets and relations among them. The word “computer,” for example, is in two different synsets. One of these synsets contains, besides “computer,” the synonyms “computing machine,” “computing device,” “data processor,” “electronic computer,” and “information processing system.” The other synset contains the synonyms for the older use of the word “computer” (when it referred to humans doing the computing), namely, “calculator,” “reckoner,” “figurer,” and “estimator.” A synset may also be accompanied by a short definition, called a “gloss,” which provides a meaning for the words in the synset. For the first synset, the gloss is “a machine for performing calculations automatically.” For the second, the gloss is “an expert at calculation (or at operating calculating machines).” Sometimes, the gloss also contains an example sentence to illustrate typical usage. Synsets are connected to other synsets using relations similar to those used in semantic networks. One such relation is called a “hypernym,” corresponding (roughly) to “is a kind of.” For example, the hypernym of our synset containing “computer” and “computing device,” etc. is the synset containing the word “machine” (and possibly other words too) having the gloss “any mechanical or electrical device that transmits or modifies energy to perform or assist in the performance of human tasks.” A “hyponym,” corresponding (roughly) to “is a general case of,” is the opposite of a hypernym. The “computer” synset just mentioned has several hyponyms, among them are ones containing the words “digital computer” and any of its synonyms (a computer that represents information by numerical digits), “number cruncher” and any of its synonyms (a computer capable of performing a large number of mathematical operations per second), “Turing machine” and any of its synonyms (a hypothetical computer with an infinitely long memory tape), and others. There are other relations also. For synsets containing nouns there is a relation called a “meronym,” corresponding to “has as parts.” The synset containing “computer” and “computing device,” etc. has several meronyms, among them are ones containing the words “chip” and its synonyms (all with the gloss “electronic equipment consisting of a small crystal of a silicon semiconductor fabricated to carry out a number of electronic functions in an integrated circuit”), “monitor” and its synonyms (all with the gloss “display produced by a device that takes signals and displays them on a television Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 445
- 446. 26 Reasoning and Representation screen or a computer monitor”), and several others. A “holonym” is the opposite of a meronym. Each synset also mentions the part of speech of the words it contains: noun, verb, adjective, or adverb. The relations among synsets differ somewhat depending on the part of speech. For example, verb synsets have a relation called “entailment.” For example, one of the synsets for the verb “walk” (use one’s feet to advance; advance by steps) entails the synset containing the verb “step” (shift or move by taking a step).27 According to its Web site (as of this writing), WordNet contains 155,287 words and 117,659 synsets. It is being maintained and expanded at Princeton and is freely and publicly available for download. Besides its use as an online dictionary and thesaurus, it is being used to support automatic text analysis, in natural language processing applications, as a knowledge base for question answering, and in semantic Web applications. Similar “wordnets” have been created in dozens of other languages. WordNet’s use as an ontology in a taxonomic knowledge base depends on the hypernym/hyponym relationships among the noun synsets and on WordNet’s use of an inheritance mechanism to infer properties of objects represented by synsets from the properties of their ancestors. For example, one chain (from specific to general) in such a hierarchy is the following: workstation → digitalcomputer → computer → machine → device → instrumentality → artifact → . . . Of course, there are side branches along this chain (which you can explore using WordNet’s online search facility). Some modifications may be needed when using WordNet as an ontology, however, because, according to Wikipedia, “. . . it contains hundreds of basic semantic inconsistencies such as (i) the existence of common specializations for exclusive categories and (ii) redundancies in the specialization hierarchy,” among other things.28 In a related effort, Karin Kipper Schuler has created “VerbNet.”29 According to a Web page about it,30 VerbNet “is the largest on-line verb lexicon currently available for English. It is a hierarchical domain-independent, broad-coverage verb lexicon with mappings to other lexical resources such as WordNet, Xtag, and FrameNet.” 26.3.3 Cyc In 1984, realizing that a large amount of common-sense knowledge would be needed for many AI applications, especially for natural language understanding, Stanford professor Douglas Lenat (1950– ), who had previously done work on automating the discovery of mathematical concepts and 446 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 447. 26.3 Semantic Networks heuristics,31 decided to undertake the immense task of providing computers with common-sense knowledge. (See Fig. 26.5.) The first step, he thought, would be to “prime the pump with the millions of everyday terms, concepts, facts, and rules of thumb” that comprise common sense. As he later described it, the project began this way:32 In the fall of 1984, Admiral Bobby Ray Inman convinced me that if I was serious about taking that first step, I needed to leave academia and come to his newly formed MCC (Microelectronics and Computer Consortium) in Austin, Texas, and assemble a team to do it. The idea was that over the next decade dozens of individuals would create a program, Cyc, with common sense. We would “prime the knowledge pump” by handcrafting and spoon-feeding Cyc with a couple of million important facts and rules of thumb. The name “Cyc” (pronounced like “psych”) comes from three letters in the middle of the word “encyclopedia.” The idea was that if Cyc had enough knowledge to understand articles in an encyclopedia, it would be able to read all kinds of material and acquire additional knowledge on its own – a “second step” toward smart computers. (The “third step” would be to conceive of and perform experiments to gain more knowledge, that is, knowledge beyond what humans already know.) To understand encyclopedia articles, humans must already know quite a bit about the world. As Lenat put it:33 If we take any sentence from an encyclopedia article and think about what the writer assumes the reader already knows about the world, we will have something worth telling Cyc. Alternatively, we can take a paragraph and look at the “leaps” from one sentence to the next and think about what the writer assumes the reader will infer “between” the sentences. [Consider, for example, the sentences] “Napoleon died on St. Helena. Wellington was greatly saddened.” The author expects the reader to infer that Wellington heard about Napoleon’s death, that Wellington outlived Napoleon, and so on. Just how much knowledge would Cyc need to have to understand articles in an encyclopedia? Lenat recently told me that he originally thought Cyc would need “a couple million general assertions, such as ‘mammals have hair’ (plus a vastly larger number of specific facts, such as what the capital of California is).” Now he believes that “the number is more like 200 million.”34 Lenat and his team of programmers and “knowledge enterers” worked away on Cyc, entering knowledge by hand, for about ten years at MCC. Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 447
- 448. 26 Reasoning and Representation Figure 26.5: Douglas Lenat (top) and the structure of the Cyc knowledge base (bottom). (Photograph courtesy of Douglas Lenat.) Several reports, a number of papers, and a book were written describing the project and its goals.35 In 1994, partly because of difficulties at MCC, Lenat founded Cycorp in Austin, Texas , “to research, develop, and commercialize Artificial Intelligence.” Work on Cyc continues there under Lenat and his staff. The language Cyc uses for representing knowledge is called CycL, an extension of the first-order predicate calculus. The object classes that Cyc knows about are arranged in a taxonomic hierarchy (such as the one shown in Fig. 26.5), which permits object classes to inherit the properties of object classes higher in the hierarchy. In Cyc’s hierarchy, for example, an “event class” such as “turning on a light switch” is a subclass of a “temporal-thing,” which is a subclass of an “individual,” which is a subclass of the most general 448 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 449. 26.3 Semantic Networks class in Cyc, namely, a “thing.” Cyc uses “rules” (stated in its logical language) to describe relationships among objects. For example, an English version of one of its rules is “For all events A and B, A causes B implies A precedes B.” Cyc’s knowledge base (KB) is divided into thousands of “micro-theories” – collections of concepts and facts about some circumscribed area. For example, one micro-theory contains knowledge about European geography. Others are devoted to expert knowledge about “chemistry, biology, military organizations, diseases, and weapon systems.” Each micro-theory is consistent, although the entire Cyc knowledge base, taken as a whole, might have contradictions. Cyc’s KB contains over five-million general assertions.36 Most of these capture common-sense knowledge about “the objects and events of everyday human life, such as buying and selling, kinship relations, household appliances, eating, office buildings, vehicles, time, and space.” In addition, the KB contains grammatical and lexical knowledge needed for natural language processing. Cyc uses an “inference engine” to conclude new facts from other existing facts and rules in its KB. Two main inference methods are used. One is the inference rule called resolution, which I mentioned in Section 11.1. To reason efficiently with resolution, Cyc has developed some proprietary heuristics and restricts the scope of its search processes by its use of micro-theories. The other inference method is property inheritance, as is commonly used in semantic network representations. “Cyc also over 1,000 special-purpose inferencing modules for handling specific classes of inference. One such module handles reasoning concerning collection membership/disjointness. Others handle equality reasoning, temporal reasoning, and mathematical reasoning. . . CycL uses a form of circumscription. . . and can make use of the closed world assumption where appropriate.”37 Cycorp is working on several applications, including intelligent search and information retrieval from the World Wide Web and natural language understanding. Its Web site claims that it “is now a working technology with applications to many real-world business problems.” Yet, there are several criticisms of Cyc. It gets stumped on some reasoning problems that humans find easy. Its vast knowledge base makes some of its reasoning impractically slow (and it will undoubtedly get even slower as more knowledge is added). It does not have satisfactory solutions for certain representation problems that AI researchers are still struggling to solve – such as how to represent substances. Also, because most of the work on Cyc is done in a private setting, it is not generally available for peer evaluation. Although Cyc has pretensions of having enough knowledge to understand natural language, it cannot yet automatically (that is, without interaction by the user) adequately translate typical English questions into CycL. To query Cyc, one must either use the cumbersome CycL language or work in an interactive fashion. Lenat described to me a working example of such interaction employed at the Cleveland Clinic where medical researchers use Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 449
- 450. 26 NOTES Cyc to get information from patient databases.38 The researcher types in his or her query in English. “Cyc parses what it can, recognizing some portions of the query, and presents the user with a set of partial query ‘fragments’ that are like fill-in-the-blank templates.” Cyc then uses its specialized knowledge about medicine plus its common-sense knowledge to figure out how to paraphrase the query for the user to check. On getting user agreement about what the user asked, it uses its knowledge about how the database is organized to generate database queries to retrieve the desired information. There are two versions of Cyc available for download. One is called ResearchCyc and is available to the research community (for research-only purposes) under a ResearchCyc license.39 Besides the Cyc Inference Engine, it contains “nearly 3,000,000 assertions (facts and rules), using 26,000+ relations, that interrelate, constrain, and, in effect, (partially) define the concepts.” Another, called OpenCyc, is a publicly available open source version of the Cyc technology.40 It contains “hundreds of thousands of terms, along with millions of assertions relating the terms to each other. . . .” One can also examine the concept hierarchy in OpenCyc using an online browser.41 No one knows exactly how humans organize and use their common-sense (and expert) knowledge. Whether the facts and relations already amassed (and yet to be gathered) by the Cyc project will be adequate in amount and organization to permit human-level reasoning has yet to be demonstrated. Yet, I applaud the effort and wish the project well. Certainly, I think something at least as ambitious as Cyc will be required. (Another attempt to gather common-sense knowledge is that of the “Commonsense Computing Initiative” at the MIT Media Lab. The work there is described at http://xnet.media.mit.edu/.) It’s possible that Cyc might get to the point where (with some human help) it will be able to gather more of the required knowledge directly from the Internet. Lenat mentions42 a game called “FACTory,” designed to help gather knowledge from humans who play the game. You can play it at http://game.cyc.com/game.html. In the game, Cyc generates natural language statements it has gathered from English sentences it has found on the Web. It presents these statements to ten randomly chosen players of the game. If enough of them answer that the statement is “true,” Cyc adds that fact to its KB (and the players get points in the game). I tried the game, and Cyc asked me whether or not “All spaghetti marinara includes some garlic.” I answered “true,” and Cyc said that I agreed with 66% of the other players and that it now (therefore) believes the sentence is “true.” Notes 1. Margery Cuyler, That’s Good! That’s Bad!, New York: Henry Holt and Co., 1991. [435] 450 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 451. 26.3 NOTES 2. Raymond Reiter, “A Logic for Default Reasoning,” Artificial Intelligence, Vol. 13, pp. 81–132, 1980. [437] 3. John McCarthy, “Circumscription – A Form of Non-monotonic Reasoning,” Artificial Intelligence, Vol. 13, pp. 27–39, 1980. There are several papers about circumscription by Vladimir Lifschitz, for example, “On the Satisfiability of Circumscription,” Artificial Intelligence, Volume 28, No. 1, pp. 17–27, 1986. [438] 4. See, for example, John McCarthy, “Applications of Circumscription to Formalizing Common Sense Knowledge,” Artificial Intelligence, Vol. 28, No. 1, pp. 89–116, 1986. [438] 5. For people who are interested in the history of the problem, the major players, and the technical details of its alleged solution, see M. Kamermans and Tijn Schmits, “The History of the Frame Problem,” available online from http://student.science.uva.nl/∼tschmits/Bachelorproject/index.html, 2004. (The paper has a nice chart summarizing this history, which is available separately at http://student.science.uva.nl/∼tschmits/Bachelorproject/poster HotFP.PNG. See also Murray Shanahan, Solving the Frame Problem: A Mathematical Investigation of the Common Sense Law of Inertia, Cambridge, MA: MIT Press, 1997. [438] 6. See, for example, papers in collections by Matt Ginsberg (ed.), Readings in Nonmonotonic Reasoning, Los Altos, CA: Morgan Kauffman Publishers, 1987, and D. Gabbay, C. Hogger, and J. Robinson (eds.), Handbook of Logic in Artificial Intelligence and Logic Programming, Vol. 3, Oxford and New York: Oxford University Press, 1994. [438] 7. I found the wave equation on a Web site belonging to a manufacturer of surfing products: http://www.waveequation.com/wave equation data.html. [439] 8. Scott E. Fahlman, “A Planning System for Robot Construction Tasks,” Artificial Intelligence, Vol. 5, No. 1, pp. 1–49, 1974. The thesis is available online as an MIT AI Laboratory Technical Report No. 283 with the same title and dated May 1973 at http://dspace.mit.edu/bitstream/handle/1721.1/6918/AITR-283.pdf?sequence=2. [439] 9. Johan de Kleer, “Qualitative and Quantitative Knowledge in Classical Mechanics,” Artificial Intelligence Laboratory, Technical Report 352, December 1975. Available online at http://dspace.mit.edu/bitstream/handle/1721.1/6912/AITR-352.pdf?sequence=2. (Some of his ideas were described in his master’s thesis proposal. See http://www2.parc.com/spl/members/dekleer/Publications/ QualitativeandQuantitativeKnowledgeinClassicalMechanics.pdf.) [440] 10. Patrick J. Hayes,“The Naive Physics Manifesto,” in D. Michie (ed.), Expert Systems in the Micro-Electronic Age, pp. 242–270, Edinburgh: Edinburgh University Press, 1979. [440] 11. Patrick J. Hayes, “The Second Naive Physics Manifesto,” in Jerry R. Hobbs and Robert C. Moore (eds.), Formal Theories of the Commonsense World, pp. 1–36, Norwood, NJ: Ablex Publishing Corporation, 1985. [440] 12. Patrick J. Hayes, “Naive Physics 1: Ontology for Liquids,” in Jerry R. Hobbs and Robert C. Moore (eds.), Formal Theories of the Commonsense World, pp. 71–107, Norwood, NJ: Ablex Publishing Corporation, 1985, An early version appeared as Memo 35 of the Institut pour les Études Semantiques et Cognitives, Université de Genève, 1978. [440] 13. The group’s Web page is at http://www.qrg.northwestern.edu/. [441] 14. The Web page for the Texas group is at http://www.cs.utexas.edu/∼qr/. [441] 15. See, for example, Artificial Intelligence, Vol. 51, Nos. 1–3, October 1991; IEEE Expert: Intelligent Systems and Their Applications, Vol. 12, No. 3, May/June 1997; the introductory article by Yumi Iwasaki, “Real World Applications of Qualitative Reasoning: Introduction to the Special Issue,” AI Magazine, Vol. 24, No. 4, pp. 16–21, Winter 2003 (a preprint of which is available online at http://ksl-web.stanford.edu/people/iwasaki/my-intro.ps); Benjamin J. Kuipers, Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge, Cambridge, MA: MIT Press, 1994; and Daniel S. Weld and Johan de Kleer, Readings in Qualitative Reasoning Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 451
- 452. 26 NOTES about Physical Systems, San Francisco: Morgan Kaufmann Publishers, 1990. [441] 16. See Ronald J. Brachman and Hector J. Levesque, Knowledge Representation and Reasoning, San Francisco: Morgan Kaufmann Publishers, 2004. [442] 17. Ronald J. Brachman, “A Structural Paradigm for Representing Knowledge,” Ph.D. dissertation, Division of Engineering and Applied Physics, Harvard University, Cambridge, MA, 1977. [442] 18. William A. Woods, “What’s in a Link: Foundations for Semantic Networks,” in Daniel Bobrow and Allan Collins (eds.), Representation and Understanding: Studies in Cognitive Science, pp. 35–82, New York: Academic Press, 1975. [443] 19. Ronald J. Brachman and James G. Schmolze, “An Overview of the KL-ONE Knowledge Representation System,” Cognitive Science: A Multidisciplinary Journal, Vol. 9, No. 2, pp. 171–216, 1985. [443] 20. Ronald J. Brachman, Richard E. Fikes, and Hector J. Levesque, “KRYPTON: A Functional Approach to Knowledge Representation, IEEE Computer, Vol. 16, No. 10, pp. 67–73, October 1983. Reprinted in Ronald J. Brachman and Hector J. Levesque (eds.), Readings in Knowledge Representation, pp. 411–429, San Francisco: Morgan Kaufmann Publishers, 1985. [443] 21. Ibid, pp. 68–69. [443] 22. For a Web site with much information and resources about description logics, see http://dl.kr.org/. [443] 23. Deborah L. McGuinness, Richard Fikes, Lynn Andrea Stein, and James Hendler, “DAML-ONT: An Ontology Language for the Semantic Web,” in Dieter Fensel, Jim Hendler, Henry Lieberman, and Wolfgang Wahlster (eds.), The Semantic Web: Why, What, and How, Cambridge, MA: MIT Press, 2002; available online at http://www.ksl.stanford.edu/people/dlm/papers/daml-ont-semantic-web.htm. [443] 24. Deborah L. McGuinness and Frank van Harmelen, “OWL Web Ontology Language Overview,” W3C Recommendation, February 10, 2004; available online at http://www.w3.org/TR/owl-features/. [443] 25. Christine Fellbaum (ed.), WordNet: An Electronic Lexical Database, Cambridge, MA: MIT Press, 1998. See also the WordNet Web site at http://wordnet.princeton.edu/ and the Wikipedia article at http://en.wikipedia.org/wiki/WordNet. [444] 26. George A. Miller et al., “Introduction to WordNet: An On-line Lexical Database,” International Journal of Lexicography, Vol. 3, No. 4, pp. 235–244, 1990. [444] 27. The reader interested in details about WordNet might refer to the WordNet Web site and to the set of five papers appearing in International Journal of Lexicography, Vol. 3, No. 4, 1990. [446] 28. http://en.wikipedia.org/wiki/WordNet. [446] 29. Karin Kipper Schuler, “VerbNet: A Broad-Coverage, Comprehensive Verb Lexicon,” University of Pennsylvania Ph.D. dissertation, 2005. Online version available from http://repository.upenn.edu/dissertations/AAI3179808/. [446] 30. http://verbs.colorado.edu/∼mpalmer/projects/verbnet.html. [446] 31. Douglas B. Lenat, “AM: Discovery in Mathematics as Heuristic Search,” in Randall Davis and Douglas B. Lenat (eds.), Knowledge-Based Systems in Artificial Intelligence, pp. 1–225, New York: McGraw-Hill, 1982; Douglas B. Lenat, “Eurisko: A Program Which Learns New Heuristics and Domain Concepts,” Artificial Intelligence, Vol. 21, Nos. 1–2, 61–98, 1983. [447] 32. From an article by Lenat in David G. Stork (ed.), Hal’s Legacy: 2001’s Computer as Dream and Reality, Cambridge, MA: MIT Press, 1998. The article is available online at 452 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 453. 26.3 NOTES http://www.cyc.com/cyc/technology/halslegacy.html. [447] 33. Ibid. [447] 34. E-mail communication, January 24, 2008. [447] 35. The main paper is Douglas B. Lenat et al., “Cyc: Toward Programs with Common Sense,” Communications of the ACM, Vol. 33, No. 8, pp. 30–49, August 1990. The first five years of the Cyc project is described in Douglas B. Lenat and R. V. Guha, Building Large Knowledge-Based Systems, Reading, MA: Addison-Wesley, 1990. [448] 36. January 13, 2009, e-mail from Doug Lenat. [449] 37. The reader who is interested in more details about how Cyc represents knowledge and how Cyc reasons can view a set of tutorial slides available online at http://www.opencyc.org/releases/doc/tut/index html?tree-e= eJyLLWTUCOVxhAJnIwPbQiYE38Ux2baQOVUPALHZCUs#AAAAAAAADAc=. [449] 38. E-mail communication, January 25, 2008. [450] 39. See http://researchcyc.cyc.com/. [450] 40. See http://opencyc.org/. [450] 41. http://www.cycfoundation.org/concepts. [450] 42. E-mail communication, January 24, 2008. [450] Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937 453
- 454. 26 NOTES 454 Copyright c 2010 Nils J. Nilsson http://ai.stanford.edu/∼nilsson/ All rights reserved. Please do not reproduce or cite this version. September 13, 2009. Print version published by Cambridge University Press. http://www.cambridge.org/us/0521122937
- 455. 27.1 Chapter 27 Other Approaches to Reasoning and Representation 27.1 Solving Constraint Satisfaction Problems In addition to reasoning methods based on logic or semantic networks, several other techniques have been explored. In this section, I’ll describe a class of problems called constraint satisfaction problems (or assignment problems) and methods for solving them. In these problems, we have a set of objects that must be assigned values that satisfy a set of constraints. We have already seen one example of an assignment problem – that of assigning labels to lines in an image. In that problem, the constraint is that each line in the image can be assigned one and only one label. Constraints can be expressed in the form of d