Artificial intelligence and knowledge representation

1. Artificial Intelligence & Knowledge Representation
National Institute Of Science & Technology Sudeep Misra [1]
Artificial Intelligence and
Knowledge Representation
Under the Guidance of
Mr. Anisur Rahman
Presented by
Sudeep Misra

2. Artificial Intelligence & Knowledge Representation
WHAT MAKES THE COMPUTER
INTELLIGENT?
 Speed of computation
 Filters out and displays only meaningful
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responses or solutions to a specific
question
 Algorithms splits task into subtasks –
recursion
 Neural networks.

3. Artificial Intelligence & Knowledge Representation
WHY ARTIFICIAL INTELLIGENCE
National Institute Of Science & Technology Sudeep Misra [3]
 Unlike humans, computers have trouble understanding
specific situations, and adapting to new situations.
 Artificial Intelligence improves machine behavior in
tackling such complex tasks, based on abstract thought,
high-level deliberative reasoning and pattern
recognition.
 Artificial Intelligence can help us understand this
process by recreating it, then potentially enabling us to
enhance it beyond our current capabilities

4. Artificial Intelligence & Knowledge Representation
KNOWLEDGE REPRESENTATION?
National Institute Of Science & Technology Sudeep Misra [4]
EXAMPLE: -CANNIBAL-MISSIONARY PROBLEM
Three missionaries and three cannibals come to a
river and find a boat that holds two. If the
cannibals ever outnumber the missionaries on
either bank, the missionaries will be eaten. How
shall they cross? Here comes the importance of
knowledge. This problem can although be solved
by intelligent algorithms but knowledge plays the
most crucial part

5. Artificial Intelligence & Knowledge Representation
Need for formal languages
Consider an English sentence like:
“The boy saw a girl with a telescope”
Natural languages exhibit ambiguity
Not only does ambiguity make it difficult for us to
understand what is the intended meaning of certain phrases
and sentences but also makes it very difficult to make
inferences
Symbolic logic is a syntactically unambigious knowledge
representation language (originally developed in an attempt
to formalize mathematical reasoning)
National Institute Of Science & Technology Sudeep Misra [5]

6. Artificial Intelligence & Knowledge Representation
KNOWLEDGE REPRESENTATION
TECHNIQUES IN AI
PROPOSITIONAL LOGIC
declarative statement
~ -> Negation
→ -> implication
↔ -> implies and implied by
v -> disjunction
^ -> Conjunction
propositional logic
= sentences represent whole propositions
“2 is prime.” P
“I ate breakfast today.” Q
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7. Artificial Intelligence & Knowledge Representation
Syntax
syntax
= how a sentence looks like
Sentence -> AtomicSentence | ComplexSentence
AtomicSentence -> T(RUE) | F(ALSE) | Symbols
ComplexSentence -> ( Sentence ) | NOT Sentence |
Connective -> AND | OR | IMPLIES | EQUIV(ALENT)
Sentence Connective Sentence
Symbols -> P | Q | R | ...
Precedence: NOT AND OR IMPLIES EQUIVALENT
conjunction disjunction implication equivalence
negation
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8. Artificial Intelligence & Knowledge Representation
Semantics
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semantics
= what a sentence means
interpretation:
assigns each symbol a truth value, either t(rue) or f(alse)
the truth value of T(RUE) is t(rue)
the truth value of F(ALSE) is f(alse)
truth tables (“compositional semantics”)
the meaning of a sentence is a function of the meaning of its
parts

9. Artificial Intelligence & Knowledge Representation
Terminology
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A sentence is valid if it is True under all possible assignments of
True/False to its propositional variables (e.g. P_:P)
Valid sentences are also referred to as tautologies
A sentence is satisfiable if and only if there is some assignment
of
True/False to its propositional variables for which the sentence is
True
A sentence is unsatisfiable if and only if it is not satisfiable (e.g.
P^:P)

10. Artificial Intelligence & Knowledge Representation
Examples
either I go to the movies or I go swimming
2 is prime implies that 2 is even
2 is odd implies that 3 is even
(inclusive vs. exclusive OR)
(implication does not imply causality)
(false implies everything)
National Institute Of Science & Technology Sudeep Misra [10]

11. Artificial Intelligence & Knowledge Representation
Semantic Networks
National Institute Of Science & Technology Sudeep Misra [11]
l Graph structures that encode taxonomic
knowledge of objects and their properties
– objects represented as nodes
– relations represented as labeled edges
l Inheritance = form of inference in which
subclasses inherit properties of superclasses

12. Artificial Intelligence & Knowledge Representation
Frames
A limitation of semantic networks is that
additional structure is often necessary to
distinguish
– statements about an object’s relationships
– properties of the object
A frame is a node with additional structure
that facilitates differentiating relationships
between objects and properties of objects.
Called a “slot-and-filler” representation
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13. Artificial Intelligence & Knowledge Representation
NORMAL Form in predicate LOGIC:
Rule:-
1. Replace and by using equivalent formulas.
2. Repeated use of negation ~ (~ p)=F.Demorgan’s law to
National Institute Of Science & Technology Sudeep Misra [13]
bring negation in front of each atom. ~ (GF)=
~G~F.Use ~x F(x)= x~F(x) and ~xF(x) = x~F(x)
Then use all the equivalent expressions to bring the
quantities in front of the expressions

14. Artificial Intelligence & Knowledge Representation
Resolution in predicate LOGIC
i) R(a)
ii) R(x) M(x,b)
First replace a in place of x in 2nd premise and conclude
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M(a,b).
e.g.
1. Marcus was a man. Man (marcus)
2. Marcus was a Pompeian. Pompeian (Marcus)
3. Caesar was a ruler. Ruler (Caesar)

15. Artificial Intelligence & Knowledge Representation
Nonmonotonic Reasoning
 Collection of true facts never decreases
 Facts changes with time
 According to the human problem solving
approach the truth status of the collected
facts may be revised based on contrary
evidences.
 Hence the nonmonotonic reasoning system
is more effective in many practical problems
solving situations.
National Institute Of Science & Technology Sudeep Misra [15]

16. Artificial Intelligence & Knowledge Representation
Principles of NMRs
 If x is not known, then conclude y
 If x cannot be proved, then conclude y
 e.g. 1: To build a program that generates a
solution to a fairly a simple problem.
 e.g. 2: To find out a time at which three busy
can all attain a meeting
 dependency-directed backtracking
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17. Artificial Intelligence & Knowledge Representation
Necessity of NMR
National Institute Of Science & Technology Sudeep Misra [17]
1. The presence of incomplete information requires
default reasoning.
2. A changing world must be decided by a
changing database.
3. Generating a complete solution to a problem
may require temporary assumption about partial
solution.

18. Artificial Intelligence & Knowledge Representation
Applications of AI
National Institute Of Science & Technology Sudeep Misra [18]
1. PATTERN RECOGNISATION
2. ROBOTICS
3. NATURAL LANGUAGE PROCESSING
4. ARTIFICIAL LIFE
5. APPLICATIONS OF AI, BY INTELLIGENT ALGORITHMS
5.1 Mechanical translation
5.2 Game playing
5.3 Computer vision
5.4 Computer hearing
5.5 Creating original thoughts or works of art
5.6 Analogical thinking Learning

19. Artificial Intelligence & Knowledge Representation
Fundamental Problems of AI
1. The ability of even the most advanced of currently existing
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computer systems to acquire information all by itself is still
extremely limited.
2. It is not obvious that all human knowledge is encodable in
“information structures” however complex. e.g. A human may
know, for example, just what kind of emotional impact touching
another person’s hand will have both on the other person and on
himself.
3. The hand-touching example will do here too, there are some things
people come to know only as a consequence of having been treated
as human beings by other human beings.
4. The kinds of knowledge that appear superficially to be
communicable from one human being to another in language alone
are in fact not altogether so communicable

20. Artificial Intelligence & Knowledge Representation
National Institute Of Science & Technology Sudeep Misra [20]
Thank You!!!