artificial intelligence and knowledge representation
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Artificial intelligence and knowledge representationTRANSCRIPT
Artificial Intelligence & Knowledge Representation N
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Sudeep Misra [1]
Artificial Intelligence and Knowledge Representation
Under the Guidance of
Mr. Anisur Rahman
Presented by
Sudeep Misra
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Sudeep Misra [2]
WHAT MAKES THE COMPUTER
INTELLIGENT? Speed of computation Filters out and displays only meaningful
responses or solutions to a specific question
Algorithms splits task into subtasks – recursion
Neural networks.
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WHY ARTIFICIAL INTELLIGENCE
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
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KNOWLEDGE REPRESENTATION?
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
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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)
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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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Syntaxsyntax= how a sentence looks likeSentence -> AtomicSentence | ComplexSentenceAtomicSentence -> T(RUE) | F(ALSE) | SymbolsComplexSentence -> ( Sentence ) | NOT Sentence |Connective -> AND | OR | IMPLIES | EQUIV(ALENT)Sentence Connective SentenceSymbols -> P | Q | R | ...Precedence: NOT AND OR IMPLIES EQUIVALENTconjunction disjunction implication equivalencenegation
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Semantics
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
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Terminology
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)
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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)
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Semantic Networks
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
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FramesA 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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NORMAL Form in predicate LOGIC:
Rule:-
1. Replace and by using equivalent formulas.
2. Repeated use of negation ~ (~ p)=F.Demorgan’s law to 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
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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 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)
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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.
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Sudeep Misra [16]
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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Necessity of NMR
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.
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Applications of AI
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
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Sudeep Misra [19]
Fundamental Problems of AI1. The ability of even the most advanced of currently existing
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
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Sudeep Misra [20]
Thank You!!!