knowledge representation in artificial intellegence.ppt

The importance of knowledge
representation
 Contrary to the beliefs of early workers in
AI, experience has shown that Intelligent
Systems cannot achieve anything useful
unless they contain a large amount of
real-world - probably domain-specific -
knowledge.
 Humans almost always tackle difficult
real-world problems by using their
resources of knowledge - "experience",
"training" etc.

The importance of knowledge
representation
 This raises the problem of how
knowledge can be represented inside a
computer, in such a way that an AI
program can manipulate it.
 Some knowledge representation
formalisms that have featured in
intelligent systems:

Knowledge rep. formalisms
 Production rules
 Formal logic, and languages based on it
(e.g. PROLOG)
 Structured objects:
 Semantic nets (or networks)
 Frames, and object-orientated
programming, which was derived from
frames
 Other similar objects, such as Scripts

Knowledge rep. formalisms
 We have already examined Production
rules (or "Rule-based reasoning") in
some detail. We will now look at some
other formalisms.

Formal logic, and languages
based on formal logic
 Logic, which was originally just the study
of what distinguishes sound argument
from unsound argument, has developed
(over many centuries) into a powerful
and rigorous system whereby true
statements can be discovered, given
other statements that are already known
to be true.

 From the point of view of AI, and other
branches of computer science, logic is
valuable because it provides
 a language, for knowledge representation,
with a well-defined, non-ambiguous,
semantics (i.e. system of meanings).
 a well-defined proof theory; there are reliable
techniques in formal logic for establishing
that an argument (i.e. a set of deductions
from statements known to be true) is sound.

 This makes logic a "gold standard"
 Other knowledge representations can
be evaluated according to whether they
produce the same results as formal
logic, on a particular reasoning task.
 If they produce a different result, there's
something wrong with them.

 There are various forms of logic, of
which the simplest is probably
propositional calculus (also known as
sentence logic), and the most commonly
used in AI is first order predicate
calculus (also known as first order
predicate logic).

Propositional calculus
 Propositional calculus is built out of
simple statements called propositions
which are either true or false.
 Example: “London is a city” is a
proposition. So is “Ice is hot”.

 These are joined together to form more
complex statements by logical
connectives, expressing simple ideas
such as and, or, not, if…then….

 There are standard symbols for these:
stands for “and”,
 stands for “or”,
 stands for “not”,
 stands for “if … then …”,
 stands for “if and only if”.

Logic: propositional calculus
 example of a statement written in
propositional calculus:
Suppose that R stands for “It is raining”, G
stands for “I have got a coat”, W stands
for “I will get wet”. The statement
R G W
is a way of writing "If it is raining and I
have not got a coat, then I will get wet."

Logic: predicate calculus
 Predicate calculus can make statements
about objects, and the properties of objects,
and the relationships between objects
(propositional calculus can’t).
 It contains predicates – statements like this:
a(S)
or this: b(S, T)
that mean S has the property a, or S and T
are connected by the relationship b.

 Example of a statement written in predicate
calculus:
Suppose that c stands for "the cat",
m stands for "the mat", s stands for "sits
on", b stands for "black", f stands for "fat", h
stands for "happy". The statement
(f(c) b(c) s(c,m))  h(c)
is a way of writing "If the fat black cat sits
on the mat then it is happy".

 As well as having the same logical
connectives as propositional calculus,
predicate calculus has two quantifiers,
 meaning “for all”,
and
 meaning “there exists”.

 Example of statement written in predicate
calculus using these quantifiers:
 Suppose that d stands for “is a day”, p stands for “is
a person”, mo stands for “is mugged on”, mi stands
for “is mugged in”, S stands for Soho, x stands for
some unspecified day and y stands for some
unspecified person.
x( d(x)  y( p(y)  mo(y, x)  mi(y, S)))
expresses the idea "Someone is mugged in
Soho every day."

 Notice that while the statement in English -
"Someone is mugged in Soho every day” - is
ambiguous, the statement written in predicate
calculus -
x( d(x)  y( p(y)  mo(y, x)  mi(y, S)))
isn’t.
 In general, translating statements from a
natural language (e.g. English) into some form
of logic forces you to sort out any ambiguity.

Formal logic
 The tools available to logicians include:
 A set of symbols indicating propositions,
predicates, variables, constants, etc
 A set of logical connectives which can be
used to combine simple terms into
compound terms, with precisely-defined
effects on the truth values involved.

Formal logic
 A set of logical equivalences, which can be
used to convert one compound term into
another, containing different connectives,
without altering its truth value.
 For instance, De Morgan’s 2nd theorem
states that ¬(PQ) is logically equivalent to
(¬P¬Q)

Formal logic
 The concepts of tautology and contradiction
(statements that are always true, and always
false, respectively).
 Some well-established rules of inference (i.e.
ways of proving an argument is sound). For
instance, if you know that CD and you know
that D isn’t true, then you know that ¬C must
be true - a rule of inference known as modus
tolens.

Formal logic
 A test of the validity of an argument using a
truth table; this is available in propositional
calculus, but not other logics.

Formal logic
 So far, this is a description of a
technique for working out arguments on
paper.
 However, some (though probably not all)
the techniques of logical manipulation
can be computerised.

Formal logic
 Computer programs have been written
which are able to perform (some of) the
operations of formal logic, and can
therefore "reason" in this way.
 This is a classic way to represent and
solve a problem in artificial intelligence.

Proving a theorem in logic
 Proving a theorem by resolution:
the stages, together with an
example proof

 The axioms, and the theorem:
 "Every rich person owns a house.
Susan is rich. Susan is a person.
Therefore Susan owns a house."

 1. Convert these statements into predicate
calculus (I've used x, y, & z for variables.
Susan is a
 constant).
x [(person(x)  rich(x))  y(house(y)  owns(x,y))].
rich(Susan).
person(Susan).
The conclusion:
z(house(z)  owns(Susan,z)).

 2. Negate the conclusion.
 This becomes: ¬z(house(z) 
owns(Susan,z)).

 3. An 8-stage process of syntactic
manipulation, designed to convert
these statements into clause form.
(a) Eliminate implications, using the
logical equivalence that a  b  ¬a  b
1st statement becomes:
x [¬(person(x)  rich(x))  y(house(y) 
owns(x,y))].

 (b) Move negations inwards (i.e., ensure that no
lines, or groups of terms, begin with ¬). Use
suitable logical equivalences such as:
¬(¬a)  a ¬(ab)  ¬a¬b ¬(ab)  ¬a¬b ¬x
P(x)  x ¬P(x) ¬x P(x)  x ¬P(x)
The 1st statement becomes: x [(¬person(x) 
¬rich(x))  y(house(y)  owns(x,y))].
The conclusion becomes: z ¬(house(z) 
owns(Susan,z)) then z ¬house(z) 
¬owns(Susan,z)).

 (c) Standardise variables so that
different quantifiers refer to different
variables.

 (d) Eliminate all existential quantifiers
("skolemisation"). This is done by
substituting a different predicate name
which is unique to the object in
question, (but which relates to the
universally-quantified class in which it is
found), rather than labelling it as an
instance of a class of objects.
1st statement becomes: x [(¬person(x)
 ¬rich(x))  (house(G(x))  owns(x,G(x))]

 (e) Eliminate all universal quantifiers, by
assuming that all variables are universally
quantified.
(¬person(x)  ¬rich(x))  (house(G(x)) 
owns(x,G(x))
The conclusion becomes:
¬house(z)  ¬owns(Susan,z)

 (f) Rewrite in conjunctive normal form.
This means groups of terms joined by
"and", the groups themselves being
terms joined by "or". Use the logical
equivalence that a(bc)  (ab)(ac)
(¬person(x)  ¬rich(x)  house(G(x))) 
(¬person(x)  ¬rich(x)  owns(x,G(x)))

 (g) Regarding statements produced
as a result of (f): since the groups are
joined by "and", they can become
separate statements in their own
right.
¬person(x)  ¬rich(x)  house(G(x))
¬person(x)  ¬rich(x)  owns(x,G(x))

 (h) Change the variable names, so that each
clause uses different variables.
We finish up with 5 clauses like this:
clause 1: ¬person(x)  ¬rich(x)  house(G(x))
clause 2: ¬person(y)  ¬rich(y)  owns(y,G(y))
clause 3: rich(Susan).
clause 4: person(Susan).
clause 5: ¬house(z)  ¬owns(Susan,z)).

 4. A cycle in which two clauses are
picked, because they can be resolved
to give a third. If the clause that results
is empty, the proof has succeeded. If
not, the new clause is added to the
others, and this stage is repeated.
Resolving clauses: pick 2 clauses which
contain the same term, negated in one
case, notnegated in the other.

 Combine them to form a new clause,
containing all the terms that were in
both the old ones, except that the term
which is present as a and ¬a is
eliminated; however, if in one case it
contains an argument (or arguments)
which is a variable and in the other case
a constant, substitute the constant for
the variable, everywhere that that
constant appears in the clause.

 Empty clause: the result of resolving
2 clauses which each only contained
one term, so that nothing remains.

 In the case of our example, the process is as
follows:
resolve 1 & 3 to give:
¬person(Susan)  house(G(Susan))
Add this to the clauses as no.6.
house(G(Susan))
Add this to the clauses as no.7.

¬person(Susan)  owns(Susan, G(Susan))
Add this as no.8.
owns(Susan, G(Susan)) Add this as no.9.
¬owns(Susan, G(Susan))
Add this as no.10.

resolve 10 & 9. This gives an empty
clause. So the proof has succeeded.

Formal logic
 Computer languages have been written
which incorporate (part of) the reasoning
mechanisms to be found in formal logic.
 The most important is Prolog.

Formal logic: Prolog
 The result is a declarative programming
language -
 a language that can (sometimes) be left to
work out the solutions to problems itself.
 It’s only necessary to provide a description
of the problem.
 This is radically different to a conventional
programming language where, unless you
incorporate an algorithm, the program is
quite incapable of solving the problem.

Formal logic
 Example of logic, used for knowledge
representation: Kowalski's project to
represent the British Nationality Act in
PROLOG.

Logic: formal methods
 Logic is used by computer scientists
when they are engaged in Formal
Methods: describing the performance of
a program precisely, so that they can
prove that it does (or doesn’t) perform
the task that it is supposed to.
 In other words, establishing the validity
of a program.

Knowledge Representation
using structured objects

 Structured objects are:
 knowledge representation formalisms
whose components are essentially similar
to the nodes and arcs found in graphs.
 in contrast to production rules and formal
logic.
 an attempt to incorporate certain
desirable features of human memory
organisation into knowledge
representations.

Semantic nets

Semantic nets
 Devised by Quillian in 1968, as a model
of human memory.
 The technique offered the possibility that
computers might be made to use words
in something like the way humans did,
following the failure of early machine-
translators.
 Organisation of semantic nets. Example:

animal
skin
fish
swimming
bird
flying
feathers
penguin canary robin
ostrich
walking
Opus
Tweety
yellow
red
white
covered_by
travels_by
isa
isa
isa isa isa isa
covered_by
travels_by
travels_by
travels_by
instance_of
instance_of
colour
colour
colour

Semantic nets
 knowledge is represented as a collection
of concepts, represented by nodes
(shown as boxes in the diagram),
connected together by relationships,
represented by arcs (shown as arrows in
the diagram).

Semantic nets
 certain arcs - particularly isa arcs - allow
inheritance of properties.
 This permits the system to "know" that a
Ford Escort has four wheels because it
is a type of car, and cars have four
wheels.

Semantic nets
 inheritance provides cognitive economy,
but there is a storage-space /
processing-time trade-off.
 This means that, if you adopt this
technique, you will use less storage
space than if you don't, but your system
will take longer to find the answers to
questions.

Semantic nets
 a semantic net should make a distinction
between types and tokens. This is why the
diagram above uses “instance_of” arcs as
well as “isa” arcs.
 Individual instances of objects have a token
node.
 Categories of objects have a type node.
 There is always at least one type node above a
token node. The information needed to define
an item is (normally) found attached to the type
nodes above it.

Semantic nets
 So far, this is just a diagram - not a
knowledgebase. But it can be converted
into a knowledgebase.

A semantic net program
written in Prolog
:- op(500, xfx, isa).
:- op(500,xfx, instance_of).
:- op(500,xfx, covered_by).
:- op(500,xfx, travels_by).
:- op(500,xfx, colour).
:- op(500,xfx, travels).
:- op(500,fx, is).
:- op(600,xfx, a).
:- op(600,xfx, an).
:- op(700, xf, ?).
:- op(500,fx, what).
:- op(600, xfx, is).

written in Prolog
:- op(650, xfx, what).
:- op(650, xfx, how).
ostrich isa bird.
penguin isa bird.
canary isa bird.
robin isa bird.
bird isa animal.
fish isa animal.
opus instance_of penguin.
tweety instance_of canary.
canary colour yellow.

written in Prolog
robin colour red.
tweety colour white.
penguin travels_by walking.
ostrich travels_by walking.
bird travels_by flying.
fish travels_by swimming.
bird covered_by feathers.
animal covered_by skin.

written in Prolog
inherit(A isa C):-
A isa C.
inherit(A isa C):-
A instance_of D,
inherit(D isa C).
inherit(A isa C):-
A isa D,
inherit(D isa C).

written in Prolog
is X a Y ? :-
inherit(X isa Y).
is X an Y ? :-
inherit(X isa Y).
inherit(A colour C):-
A colour C.
inherit(A colour C):-
(A instance_of D ; A isa D),
inherit(D colour C).

written in Prolog
what colour is A ? :-
inherit(A colour C),
nl, write(A), write(' is '),
write(C).
inherit(A covered_by C):-
A covered_by C.
inherit(A covered_by C):-
inherit(D covered_by C).

written in Prolog
A is covered_by what ? :-
inherit(A covered_by C),
nl, write(A),
write(' is covered by '),
write(C).
inherit(A travels_by C):-
A travels_by C.
inherit(A travels_by C):-
inherit(D travels_by C).

written in Prolog
A travels how ? :-
inherit(A travels_by C),
nl, write(A),
write(' travels by '),
write(C).

Semantic nets
 This is a program, written in Prolog,
which contains all the knowledge
represented in the diagram above,
together with a mechanism for finding
information by inheritance, and a
rudimentary natural language interface.

Semantic nets
 It can answer questions like
is tweety an animal ? (it answers “yes”)
what colour is tweety ? (it answers “white”)
opus is covered_by what ? (it answers
“feathers”) and so on.

Semantic nets
 It could have been written in C++ or
Java (although it would have been much
harder), or any other present-day high-
level language.

Semantic nets
 Note that I do NOT expect you to
understand the details of this program,
or to memorise it, or to be able to quote
it. It is simply there to indicate that it is
possible to store common-sense
knowledge like this, and it may even on
occasions be quite easy.

Semantic nets
 Problems with semantic nets
 logical inadequacy - vagueness about what
types and tokens really mean.
 heuristic inadequacy – finding a specific piece
of information could be chronically inefficient.
 trying to establish negation is likely to lead to
a combinatorial explosion.
 "spreading activation" search is very
inefficient, because it is not knowledge-
guided.

Semantic nets
 Attempted improvements
 building search heuristics into the
network.
 more sophisticated logical structure,
involving partitioning.
 these improvements meant that the
formalism’s original simplicity was
lost.

Semantic nets
 Developments of the semantic nets
idea:
 psychological research into whether human
memory really was organised in this way.
 used in the knowledge bases in certain
expert systems: e.g. PROSPECTOR.
 special-purpose languages have been
written to express knowledge in semantic
nets.

knowledge representation in artificial intellegence.ppt

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