CS 2710 Foundations of AI Lecture 12. Propositional logic. Logical inference problem

Transcription

1 CS 2710 Foundations of I ecture 12 ropositional logic ilos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square ogical inference problem ogical inference problem: Given: a knowledge base K (a set of sentences) and a sentence (called a theorem), oes a K semantically entail? K? In other words: In all interpretations in which sentences in the K are true, is also true? 1

2 ogical inference problem ogical inference problem: Given: a knowledge base K (a set of sentences) and a sentence (called a theorem), oes a K semantically entail? K pproaches to solve the logical inference problem: Truth-table approach Inference rules Conversion to ST Resolution refutation roperties of inference solutions Truth-table approach lind xponential in the number of variables Inference rules ore efficient any inference rules to cover logic Conversion to ST - Resolution refutation ore efficient Sentences must be converted into CNF One rule the resolution rule - is sufficient to perform all inferences 2

3 K in restricted forms If the sentences in the K are restricted to some special forms some of the sound inference rules may become complete xample: Horn form (Horn normal form) a clause with at most one positive literal ( ) ( C ) Can be written also as: ( ) (( C) ) Two inference rules that are sound and complete for Ks in the Horn normal form: Resolution odus ponens K in Horn form Horn form: a clause with at most one positive literal ( ) ( C ) Not all sentences in propositional logic can be converted into the Horn form K in Horn normal form: Two types of propositional statements: Rules ) ( 1 2 k ( 2 ( 1 k ) ) ( 1 2 k ) ropositional symbols: facts 3

4 Why K in Horn form is useful? K in Horn normal form: Rules (in implicative form): If then statements known to be true ) If Then ( 1 2 k 1. The stain of the organism is gram-positive, and 2. The morphology of the organism is coccus, and 3. The growth conformation of the organism is chains the identity of the organism is streptococcus Facts = propositions known to be true xamples: 1 or The stain of the organism is gram-positive Inferences: let us infer new true propositions, such as, or the identity of the organism is streptococcus in the rule conclusion These are referred as inferences on propositional symbols K in Horn form pplication of the resolution rule: Infers new facts from previous facts ( ), ( ), ( C) ( C) Resolution is sound and complete for inferences on propositional symbols for K in the Horn normal form (clausal form) Similarly, modus ponens is sound and complete when the HNF is written in the implicative form 4

5 Complexity of inferences for Ks in HNF uestion: How efficient the inferences in the HNF can be? nswer: Inference on propositional symbols rocedures linear in the size of the K in the Horn form exist. Size of a clause: the number of literals it contains. Size of the K in the HNF: the sum of the sizes of its elements. xample:,,( C),( C ),( C ),( F G) or,,( C),( C ),( C ),( F G) The size is: 12 Complexity of inferences for Ks in HNF How to do the inference on propositional symbols? If the HNF (is in the clausal form) we can apply resolution.,,( C),( C ),( C ),( F G) C C 5

6 Complexity of inferences for Ks in HNF How to do the inference on propositional symbols? If the HNF (is in the clausal form) we can apply resolution.,,( C),( C ),( C ),( F G) C C Inferred facts Complexity of inferences for Ks in HNF Features: very resolution is a positive unit resolution; that is, a resolution in which one clause is a positive unit clause (i.e., a proposition symbol).,,( C),( C ),( C ),( F G) C C 6

7 Complexity of inferences for Ks in HNF Features: t each resolution, the input clause which is not a unit clause is a logical consequence of the result of the resolution. (Thus, the input clause may be deleted upon completion of the resolution operation.),,( C),( C ),( C ),( F G) C C Complexity of inferences for Ks in HNF Features: t each resolution, the input clause which is not a unit clause is a logical consequence of the result of the resolution. (Thus, the input clause may be deleted upon completion of the resolution operation.),,( C),( C ),( C ),( F G) C C 7

8 Complexity of inferences for Ks in HNF Features: Following this deletion, the size of the K (the sum of the lengths of the remaining clauses) is one less than it was before the operation.),,( C),( C ),( C ),( F G) C C Complexity of inferences for Ks in HNF Features: Following the deletion, the size of the K (the sum of the lengths of the remaining clauses) is one less than it was before the operation.). Now let us see one more step,,( C),( C ),( C ),( F G) C C 8

9 Complexity of inferences for Ks in HNF Features: Following the deletion, the size of the K (the sum of the lengths of the remaining clauses) is one less than it was before the operation.) Now let us see one more step,,( C),( C ),( C ),( F G) C C Complexity of inferences for Ks in HNF Features: If n is the size of the K, then at most n positive unit resolutions may be performed on it.,,( C),( C ),( C ),( F G) C C 9

10 Complexity of inferences for Ks in HNF linear time resolution algorithm: The number of positive unit resolutions is limited to the size of the formula (n) ut to assure overall linear time we need to access each proposition in a constant time: ata structures indexed by proposition names may be accessed in constant time. (This is possible if the proposition names are number in a range (e.g., 1..n), so that array lookup is the access operation. If propositions are accessed by name, then a symbol table is necessary, and the algorithm will run in time O(n log(n)). Forward and backward chaining Two inference procedures based on modus ponens for Horn Ks: Forward chaining Idea: Whenever the premises of a rule are satisfied, infer the conclusion. Continue with rules that became satisfied. ackward chaining (goal reduction) Idea: To prove the fact that appears in the conclusion of a rule prove the premises of the rule. Continue recursively. oth procedures are complete for Ks in the Horn form!!! 10

11 Forward chaining example Forward chaining Idea: Whenever the premises of a rule are satisfied, infer the conclusion. Continue with rules that became satisfied. ssume the K with the following rules and facts: K: R1: C R2: R3: F1: F2: F3: C C F G Theorem:? Theorem: K: R1: R2: Forward chaining example C C R3: C F G F1: F2: F3: 11

12 Theorem: K: R1: R2: Forward chaining example C C R3: C F G F1: F2: F3: Rule R1 is satisfied. F4: C Theorem: K: R1: R2: Forward chaining example C C R3: C F G F1: F2: F3: Rule R1 is satisfied. F4: C Rule R2 is satisfied. F5: 12

13 13 CS 2740 Knowledge Representation Forward chaining fficient implementation: linear in the size of the K xample: Forward chaining Count the number of facts in the antecedent of the rule genda (facts)

14 14 Forward chaining Inferred facts decrease the count inferred Forward chaining New facts can be inferred when the count associated with a rule becomes 0 inferred add to agenda

15 15 Forward chaining Forward chaining

16 16 Forward chaining Forward chaining

17 ackward chaining example? C R2 K: R1: R2: C C R3: C F G F1: F2: F3: ackward chaining is more focused: tries to prove the theorem only ackward chaining example K: R1: C R2 R2: C R1 C R3: C F G F1: F2: F3: ackward chaining is more focused: tries to prove the theorem only 17

18 18 ackward chaining fficient implementation ackward chaining fficient implementation

19 19 ackward chaining fficient implementation ackward chaining fficient implementation

20 20 ackward chaining fficient implementation ackward chaining fficient implementation

21 21 ackward chaining fficient implementation ackward chaining fficient implementation

22 22 ackward chaining fficient implementation ackward chaining fficient implementation

23 Forward vs ackward chaining FC is data-driven, automatic, unconscious processing, e.g., object recognition, routine decisions ay do lots of work that is irrelevant to the goal C is goal-driven, appropriate for problem-solving, e.g., Where are my keys? How do I get into a h program? Complexity of C can be much less than linear in size of K K agents based on propositional logic ropositional logic allows us to build knowledge-based agents capable of answering queries about the world by inferring new facts from the known ones xample: an agent for diagnosis of a bacterial disease Facts: Rules: The stain of the organism is gram-positive The growth conformation of the organism is chains (If) (Then) The stain of the organism is gram-positive The morphology of the organism is coccus The growth conformation of the organism is chains The identity of the organism is streptococcus 23