1. 1 Chapter 3 Knowledge Representation

2. 344-471 AI & ESChapter 3 2 Knowledge Representation Semantic Network Network Representation Conceptual Graph Frame

3. 344-471 AI & ESChapter 3 3 Semantic Network

4. 344-471 AI & ESChapter 3 4 Network Representation

5. 344-471 AI & ESChapter 3 5 Conceptual Graph

6. 344-471 AI & ESChapter 3 6 Conceptual Graph

7. 344-471 AI & ESChapter 3 7 Case Frame

8. 344-471 AI & ESChapter 3 8 Conceptual / Graph Frame : Hotel Bed

9. 344-471 AI & ESChapter 3 9 Frame : Hotel Bed

10. 344-471 AI & ESChapter 3 10 Frame : Bird

11. 344-471 AI & ESChapter 3 11 Frame :Penguin 1 : Ambiguity

12. 344-471 AI & ESChapter 3 12 Frame :Penguin 2 : resolve ambiguity

13. 344-471 AI & ESChapter 3 13 Frame : Penguin 3 : Subclass relation

14. 344-471 AI & ESChapter 3 14 Knowledge Representation  important attribute : isa and instance  relationship among attributes  at what level of detail should the world be represented?  Mary is Sue’s cousin. เมรี่เป็นหลานของซู  Mary = daughter(brother(mother(Sue)))  Mary = daughter(sister(mother(Sue)))  Mary = daughter(brother(father(Sue)))  Mary = daughter(sister(father(Sue)))  Mary = daughter(sibling(parent(Sue)))

15. 344-471 AI & ESChapter 3 15 Knowledge Representation  John broke the window  Sequence ? pick up hard object, Hurl the object to the window  Cause hand / foot to move fast and crash into the window  Shut the window so hard that the glass breaks  Finding the right structure  John went to Sizzer last night. He ordered a large rare steak, paid his bill, and left.  Did john eat dinner last night?  John flew to New York.  John flew a kite.  John flew down the street.

16. 344-471 AI & ESChapter 3 16 Inheritable Knowledge

17. 344-471 AI & ESChapter 3 17 Frame Baseball-Player isa : Adult-Male bats: (EQUAL handed) height: 6-1 batting-average:.252 figure 4.7

18. 344-471 AI & ESChapter 3 18 Procedural Knowledge as Rules similar to figure 4.7

19. 344-471 AI & ESChapter 3 19 Redundant Representation John punched Mary Mary punched John

20. 344-471 AI & ESChapter 3 20 Similarity Net liking among set of frame

21. 344-471 AI & ESChapter 3 21 Representations man(Marcus) man(Caesar) person(Cleopatra)  X : man(X)  person(X)  y person(y) y = Cleopatra y = Marcus y = Caesar

22. 344-471 AI & ESChapter 3 22 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) and son(Y,Z) :- grandson(X,Z) 3. Complex and approximate matching

23. 344-471 AI & ESChapter 3 23 Indexing

24. 344-471 AI & ESChapter 3 24 Logic Programming  x : pet(x)  small(x)  apartment_pet(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 apartment_pet (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

25. 344-471 AI & ESChapter 3 25 Complex Indexing approximately match their precondition approximately match the current situation -> ELIZA Person: Men are all alike. ELIZA: In what way? Person: They’re always bugging us about something 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

26. 344-471 AI & ESChapter 3 26 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

27. 344-471 AI & ESChapter 3 27 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

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