ai-prolog3

# ai-prolog3 - Knowledge Representation More Logic Semantic...

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1 Knowledge Representation More Logic Semantic Networks and Frames

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2 From lecture 2. . Need formal notation to represent knowledge, allowing automated inference and problem solving. One popular choice is use of logic. Propositional logic is the simplest. Symbols represent facts: P, Q, etc. . These are joined by logical connectives (and, or, implication) e.g., P Q; Q R Given some statements in the logic we can deduce new facts (e.g., from above deduce R)
3 Predicate Logic Propositional logic isn’t powerful enough as a general knowledge representation language. Impossible to make general statements. E.g., “all students sit exams” or “if any student sits an exam they either pass or fail”. So we need predicate logic. .

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4 Predicate Logic In predicate logic the basic unit is a predicate/ argument structure called an atomic sentence: likes(alison, chocolate) tall(fred) Arguments can be any of: constant symbol, such as ‘alison’ variable symbol, such as X function expression, e.g., motherof(fred) So we can have: likes(X, richard) friends(motherof(joe), motherof(jim))
5 Predicate logic: Syntax These atomic sentences can be combined using logic connectives likes(john, mary) 2 tall(mary) tall(john) 2 nice(john) Sentences can also be formed using quantifiers 2 (forall) and 2 (there exists) to indicate how to treat variables: 2 2 X lovely(X) Everything is lovely. 2 2 X lovely(X) Something is lovely. 2 2 X in(X, garden) 2lovely(X) Everything in the garden is lovely.

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6 Predicate Logic Can have several quantifiers, e.g., 2 2 X 2 Y loves(X, Y) 2 2 X handsome(X) 2 2 Y loves(Y, X) So we can represent things like: All men are mortal. No one likes sprouts.
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## This note was uploaded on 01/20/2011 for the course CS 6810 taught by Professor Hecker during the Spring '10 term at CSU East Bay.

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ai-prolog3 - Knowledge Representation More Logic Semantic...

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