Ch 1 Introduction to AI.pdf

What is Intelligence?
 A very popular YouTube video of a 5 year old girl
speaking about KCR and his cabinet on stage
 Solving the water jug problem on the next slide
 Solving the missionaries and cannibals problem
 Sakuntala Devi multiplying in a flash
 Following Maryada Ramanna and getting in trouble
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Example: Measuring problem!
 Problem: Using these three buckets, measure 7
liters of water.
3 l 5 l
9 l

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Missionaries and Cannibals:
Initial State and Actions
 Initial state:
– all missionaries, all
cannibals, and the
boat are on the left
bank
 Goal state :
– all missionaries, all
cannibals are on the
Right bank
 Conditions
– Boat can carry at most 2
– Missionaries are in
danger if cannibals
outnumber them

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What is artificial intelligence?
• There is no clear consensus on the definition of AI
• Here’s one from John McCarthy, (He coined the phrase AI in
1956) - see http://www-formal.Stanford.edu/jmc/whatisai.html)
Q. What is artificial intelligence?
A. It is the science and engineering of making intelligent machines,
especially intelligent computer programs. It is related to the
similar task of using computers to understand human intelligence,
but AI does not have to confine itself to methods that are
biologically observable.
Q. Yes, but what is intelligence?
A. Intelligence is the computational part of the ability to achieve
goals in the world. Varying kinds and degrees of intelligence
occur in people, many animals and some machines.

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Other possible AI definitions
 AI is a collection of hard problems which can be
solved by humans and other living things, but for
which we don’t have good algorithms for solving.
– e. g., understanding spoken natural language, medical
diagnosis, learning, self-adaptation, reasoning, chess
playing, proving math theories, etc.
 Definition from R & N book: a program that
– Acts like human (Turing test)
– Thinks like human (human-like patterns of thinking steps)
– Acts or thinks rationally (logically, correctly)
 Hofstadter: AI is whatever hasn’t been done yet.

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Other possible AI definitions
 Rich & knight:
– The study of how to make programs/computers do things
that people do better
– The study of how to make computers solve problems
which require knowledge and intelligence
 Luger & Stubblefield:
– AI may be defined as the branch of computer science that
is concerned with automation of intelligent behavior.
 Marvin Minsky
– Artificial Intelligence is a science of how to persuade
computers to exhibit such a type of behaviour that
conventionally requires Human Intelligence

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Brief History of AI (1)
Symbolic AI
 1943: Production rules
 1956: “Artificial Intelligence”
 1958: LISP AI language
 1965: Resolution theorem
proving
 1970: PROLOG language
 1971: STRIPS planner
 1973: MYCIN expert system
 1982-92: Fifth generation computer
systems project
 1986: Society of mind
 1994: Intelligent agents
Biological AI
 1943: McCulloch-Pitt’s neurons
 1959: Perceptron
 1965: Cybernetics
 1966: Simulated evolution
 1966: Self-reproducing automata
 1975: Genetic algorithm
 1982: Neural networks
 1986: Connectionism
 1987: Artificial life
 1992: Genetic programming
 1994: DNA computing

8
AI Application Areas
 Game Playing
 Automated Reasoning and Theorem Proving
 Expert Systems
 Natural Language Understanding and Semantic
Modeling
 Modeling Human Performance
 Planning and Robotics
 Machine Learning
 Neural Nets and Genetic Algorithms
 Fraud Detection

9
Expert Systems(1)
 Expert systems are constructed by obtaining
the knowledge of a human expert and coding
it into a form that a computer may apply to
similar problems.
– domain expert provides the necessary knowledge
of the problem domain.
– knowledge engineer is responsible for
implementing this knowledge in a program that is
both effective and intelligent in its behavior.

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Expert Systems(2)
 Many successful expert systems
– DENDRAL
• designed to infer the structure of organic molecules from their
chemical formulas and mass spectrographic information about the
chemical bonds present in the molecules.
• use the heuristic knowledge of expert chemists to search into the
very large possible number of molecular structures.
– MYCIN
• used expert medical knowledge to diagnose and prescribe
treatment for spinal meningitis and bacterial infections of the
blood.
• Provided clear and logical explanations of its reasoning, used a
control structure appropriate to the specific problem domain, and
identified criteria to reliably evaluate its performance.

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Expert Systems(3)
 Many successful expert systems (Continued)
– PROSPECTOR
• for determining the probable location and type of
ore deposits based on geological information.
– INTERNIST
• for performing diagnosis in the area of internal
medicine.
– XCON
• for configuring VAX computers.

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ELIZA
Person: Men are all alike.
ELIZA: In what way?
Person: They’re always bugging us about something or other.
ELIZA: Can you think of a specific example?
Person: Well, my husband made me come here.
ELIZA: Your husband made you come here.
Person: He says I’m depressed much of the time.
ELIZA: I am sorry to hear you are depressed.
Person: It’s true. I am unhappy.
ELIZA: Do you think coming here will help you not to be
unhappy?
Person: I need some help, that much seems certain.
ELIZA: What would it mean to you if you got some help?
Person: Perhaps I could learn to get along with my husband.
ELIZA: Tell me more about your family.

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Turing Test
 Alan Turing (1912 - 1954)
– Proposed a test - Turing’s
Imitation Game – in his 1950
article Computing machinery
and intelligence.
 Turing test
– Computer and woman
separated from an interrogator.
– The interrogator types in a
question to either party.
– By observing responses, the
interrogator’s goal was to
identify which was the
computer and which was the
woman.
– If he fails, the computer is
intelligent!
Interrogator
Honest Woman Computer

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Turing Test – how good is it ?
 Measures imitation, not intelligence
– Does Eliza pass this test ? YES!
– Does Deep Blue pass this test ? NO!
– Most AI programs are shallow, they recognize “syntax”
but not “semantics”
 Searle’s Chinese Room
– Room with a slot, human with huge rule book on how to
translate Chinese to English
– If someone drops a Chinese letter in the slot and the
human translates it to English, does the human
understand Chinese?
 Turing test is not reproducible, constructive,
and amenable to mathematic analysis.

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Approaches to AI
human-like
performance
thought/reasoning
ideal
performance
(rationality)
behaviour
systems that
think like humans
systems that act
like humans
systems that act
rationally
systems that
think rationally
GPS
ELIZA Rational Agents
Theorem Provers

Think well
 Develop formal models of knowledge
representation, reasoning, learning,
memory, problem solving, that can be
rendered in algorithms.
 There is often an emphasis on systems that are
provably correct, and guarantee finding an optimal
solution.
 Theorem Provers

Act well
 For a given set of inputs, generate an
appropriate output that is not necessarily
correct but gets the job done.
 A heuristic (heuristic rule, heuristic method) is a
rule of thumb, strategy, trick, simplification, or any
other kind of device which drastically limits search for
solutions in large problem spaces.
 Heuristics do not guarantee optimal solutions; in fact,
they do not guarantee any solution at all: all that can
be said for a useful heuristic is that it offers
solutions which are good enough most of the time.

Act like humans
 Behaviorist approach.
 Not interested in how you get results, just the
similarity to what human results are.
 ELIZA
 Exemplified by the Turing Test (Alan Turing,
1950).

Think like humans
 Cognitive science approach
 Focus not just on behavior and I/O
but also look at reasoning process.
 Computational model should reflect “how” results
were obtained.
 GPS (General Problem Solver): Goal not just to
produce humanlike behavior (like ELIZA), but to
produce a sequence of steps of the reasoning process
that was similar to the steps followed by a person in
solving the same task.

20
Components of AI programs
 Knowledge Base
– Facts
– Rules
 Control Strategy
– Which rule to apply
 Inference Mechanism
– How to derive new knowledge from the existing
information
(follows from physical symbol system hypothesis)

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AI as Representation and Search
 In their Turing Award lecture, Newell and Simon
argue that intelligent activity, in either humans or
machines, is achieved thru
1. Symbol patterns to represent significant aspects of a
problem domain,
2. Operations on these patterns to (combine and
manipulate) generate potential solutions and
3. Search to select a solution from these possibilities.
 Physical Symbol System Hypothesis [NS’76]:
– A physical symbol system has the necessary and
sufficient means for general intelligent action.

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Physical Symbol System Hypothesis
 Physical Symbol System Hypothesis outlines the
major foci of AI research
1. Defining symbol structures and operations
necessary for intelligent problem solving and
2. Developing strategies to efficiently and correctly
search potential solution generated by these
structures and operations.
 These two interrelated issues of Knowledge
Representation and Search are at the heart of AI.
 We study these two issues in detail in this course.

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Example: Tic-Tac-Toe
program
 Complexity
 Use of generalizations
 Clarity of knowledge
 Extensibility
1 2 3
4 5 6
7 8 9

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Program 1
 Board: 9-element vector
0 : blank, 1 : X , 2 : O
 Move table: 39 Rows of 9-element vectors
– Nearly 2000 entries.
 Algorithm:
1. transform board vector from base 3 to 10
2. use (1) as the move table index
3. change the board by using the vector from (2)

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Comments:
 Advantages:
– efficient in terms of time,
– optimal game of tic-tac-toe in theory
 Disadvantages:
– space - move table space
– work - move table
– error prone - move table
– three dimension - 327, no longer work at all

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Program 2
 Board: program1
2 : blank, 3 : X, 5 : O
 Turn: game moves 1,2,3,.....
odd-numbered move : x
even-numbered move : o
Algorithm : 3 sub procedures
Make2: Determines our step to have two X’s in…
Posswin (p): 18 (3*3*2) for p = X
50 (5*5*2) for p = O
Go (n) : Place X in square [n]

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Strategy
 Turn=1 Go (1)
 Turn=2 Go (5) or Go (1)
 Turn=3 Go (9) or Go (3)
 Turn=4 Go(Posswin(X)) or Go(Make2)
 …
 Try to win if you can, check if the opponent is
winning in the next move and block him/her, else
make2 move.
 See Next Slide for all 9 moves

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Comments:
 Less efficient than Program 1 (time)
 More efficient (space)
 More clarity (strategy)
 Easier to change (strategy)
 Cannot extend to three dimension

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Program 3
 Board - magic square 15
 Possible win check:
S = sum of two paired block owned by a player
D = 15 – S
if 0 < D < 10 and Board [D] is empty then the player can
win
8 3 4
1 5 9
6 7 2

Comments:
 Program 3 will require much more time than the
other two programs
– Need to search a tree representing all possible
move sequences before making each move.
 It can be extended to handle three-dimensional
tic-tac-toe.
 It can even be extended to handle games more
complicated than tic-tac-toe.
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Comments
 Much more complex (time and space)
 Extendable
 AI technique

Current Trends in AI
 Traditional AI + BioSciences
 Soft Computing to deal with intractability, imprecision,
uncertainty, partial truth, and approximation to achieve
non-conventional solutions, robustness and low costs.
• Fuzzy Logic (FL),
• Artificial Neural Networks (ANN),
• Evolutionary Computation (EC),
• Genetic algorithms,
• Swarm Intelligence (i.e. Ant colony optimization and
Particle swarm optimization),
• Multi-agent systems etc.
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Homework
1. Give a couple of definitions of AI (state the Scientist's
name proposing the definition).
2. Write a 300 word essay about the History of AI.
3. Describe Turing test with a picture.
4. Describe the four categories of intelligent systems
identified by Russell and Norvig.
5. Describe the three main components of AI systems.
6. Describe the three solutions for Tic-Tac-Toe problem given
in the textbook and state pros and cons of these solutions.
7. Write a 300 word essay about current trends of AI.
8. List and explain the applications of Artificial Intelligence.
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Ch 1 Introduction to AI.pdf

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