Chapter 6 Representing Knowledge Using Rules Artificial Intelligence ดร. วิภาดา เวทย์ประสิทธิ์ ภาควิชาวิทยาการคอมพิวเตอร์ คณะ วิทยาศาสตร์ มหาวิทยาลัยสงขลานครินทร์

Artificial Intelligence Lecture 19Page 2 Representations  procedural representation  declarative representation man(Marcus) man(Caesar) person(Cleopatra) X : man(X) -> person(X) ? y person(y) y = Cleopatra y = Marcus y = Caesar

Artificial Intelligence Lecture 19Page 3 Logic Programming  x : pet(x)  small(x)  apartmentpet(x)  x : cat(x)  dog(x)  pet(x)  x : poodle(x)  dog(x)  small(x) poodle(fluffy) A Representation in Logic Figure 6.1: Declarative and a Procedural Representation apartmentpet (X) :- pet (X), small (X). pet (X) :- cat (X). pet (X) :- dog (X). dog (X) :- poodle (X). small (X) :- poodle (X). poodle (fluffy). A Representation in PROLOG Fact Rules

Artificial Intelligence Lecture 19Page 4 Matching 1. Indexing : problems: 1. need large number of rules 2. the precondition of rule is not always obvious 2. Matching with variables problems: many-many match problem many rules are matched against many elements in the state description simultaneously. son(X,Y) son(Y,Z) :- grandson(X,Z) 3. Complex and approximate matching

Artificial Intelligence Lecture 19Page 5 Indexing

Artificial Intelligence Lecture 19Page 6 Complex Indexing their precondition approximately match the current situation -> ELIZA Person: Men are all alike. ELIZA: In what way? Person: They’re always bugging us about somethings or other. ELIZA: Can you think of a specific example? Person: Well, my boyfriend made you come here. ELIZA: Your boyfriend made you come here. Person: He says I’m depressed much of the time. ELIZA: I am sorry to hear you depressed. Person: It’s true. I am unhappy. ELIZA: Do you think coming here will help you not to be unhappy? Person: I need some help, that much seems certain. ELIZA: What would it mean to you if you got some help? Person: Perhaps I could learn to get along with my mother. ELIZA Tell me more about your family. Figure 6.6: A Bit of a Dialogue with ELIZA

Artificial Intelligence Lecture 19Page 7 Complex Indexing (X me Y)  (X you Y) (I remember X)  (Why do remember X just now?) (My {family-member} is Y)  (Who else in you family is Y?) (X {family-member} Y)  (Tell me more about your family) Figure 6.7: Some ELIZA-like rules

Artificial Intelligence Lecture 19Page 8 Conflict Resolution 1.Preference based on Rules generalization of rules specific rule (higher priority) Bird can fly Penquin cannot fly. 2.Preference based on Objects based on important object (ELIZA) I : semantic significant everybody : rarely use 3.Preference based on States based on heuristic function

Artificial Intelligence Lecture 19Page 9 Control Knowledge  Knowledge about which parts are most likely to find the goal state.  Knowledge about which rules to apply in a given situation.  Knowledge about the order in which to pursue subgoals.  Knowledge about useful sequence of rules to apply. 1. Long term memory -> Rules 2. Short term memory -> Working memory

Artificial Intelligence Lecture 19Page 10 Control Knowledge Under conditions A and B, Rules that do {not} mention X { at all, in their left-hand side. in their right-hand side.} Will {definitely be useless, probably be useless … probably be especially useful definitely be especially useful} Figure 6.8: Syntax for a Control Rule [Davis, 1980]

Artificial Intelligence Lecture 19Page 11 End Chapter 6