17-08-2012, 03:32 PM
Artificial Intelligence and Knowledge Representation
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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
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
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)
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
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
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
Terminology
A sentence is valid if it is True under all possible assignments of
True/False to its propositional variables (e.g. 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^)
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)
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
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