1. 1 Pertemuan 8 Representing Knowledge Using Rules Matakuliah: T0264/Inteligensia Semu Tahun: Juli 2006 Versi: 2/1

2. 2 Learning Outcomes Pada akhir pertemuan ini, diharapkan mahasiswa akan mampu : >

3. 3 Outline Materi Materi 1 Materi 2 Materi 3 Materi 4 Materi 5

4. 4 6.1 Procedural vs Declarative Knowledge A declarative representation is one in which knowledge is specified, but the use to which that knowledge is to be put is not given. To use a declarative representation, we must augment it with a program that specifies what is to be done to the knowledge and how. A procedural representation is one in which the control information that is necessary to use the knowledge is considered to be embedded in the knowledge it self. To use a procedural representation, we need to augment it with an interpreter that follows the instructions given in the knowledge.

5. 5 Procedural vs Declarative Knowledge Consider the knowledge base : man(Marcus) man(Caesar) person(Cleopatra)  x : man(x)  person(x) Supose we want to answer the question  y : person(y)

6. 6 Procedural vs Declarative Knowledge We could answer with any one of : y = Marcus y = Caesar y = Cleopatra Now consider an alternative Knowledge Base : man(Marcus) man(Caesar)  x : man(x)  person(x) person(Cleopatra)

7. 7 6.2 Logic Programming PROLOG A PROLOG program is composed of a set of Horn clauses. A Horn clause is a clause that has at most one positive literal. Examples : p  p  q r  s  r  s

8. 8 Logic Programming A Declarative and a Procedural Representation A Representation in Logic  x : pet(x)  small(x)  apartmentpet(x)  x : cat(x)  dog(x)  pet(x)  x : poodle(x)  dog(x)  small(x) poodle(fluffy)

9. 9 Logic Programming A Representation in PROLOG apartmentpet(x): - pet(x), small(x). pet(x): - cat(x). pet(x): - dog(x). dog(x): - poodle(x). small(x): - poodle(x). poodle(fluffy).

10. 10 Logic Programming Answering Question in PROLOG ?- apartmentpet(x). ?- cat(fluffy). ?- cat(mittens).

11. 11 6.3 Forward vs Backward Reasoning Reasoning choose forward or backward Number of start and goal states. We would like to move from smaller set of state to the large set of state Branching factor in each direction. We would like to proceed in the direction with the lower branching factor Need to justify reasoning. If so, it is important to proceed in the direction that corresponds more closely with the way the user will think. Triggers for problem solving. If it is the arrival of new fact, forward reasoning makes sense. If it is a query to which a response is desired, backward reasoning ai more natural.

12. 12 Forward vs Backward Reasoning Sample or the Rules for Solving the 8- Puzzle Assume the areas of the tray are numbered:

13. 13 Forward vs Backward Reasoning Square 1 empty and Square 2 contains tile n  Square 2 empty and Square 1 contains tile n Square 1 empty and Square 4 contains tile n  Square 4 empty and Square 1 contains tile n Square 2 empty and Square 1 contains tile n  Square 1 empty and Square 2 contains tile n

14. 14 Forward vs Backward Reasoning An Example :

15. 15 Forward vs Backward Reasoning Forward rules, which encode knowledge about how to respond to certain input configurations. Backward rules, which encode knowledge about how to achieve particular goals.

16. 16 Forward vs Backward Reasoning A Bad Use of Heuristic Bidirectional Search

17. 17 6.4 Matching How we extract from the entire collection of rules those that can be applied at a given point ? To do so requires some kind of matching between the current state and the preconditions of the rules. How should this be done ?? - Indexing - Matching with Variable - Complex and Approximate Matching - Conflict Resolution

18. 18 Indexing One legal chess move

19. 19 Indexing Another Way to Describe Chess Move White pawn at Square(file e, rank 2) AND Square(file e, rank 3)  move pawn from is empty Square(file e, rank 2) AND to Square(file e, rank 4) Square(file e, rank 4) is empty

20. 20 Matching with Variable Many-to-Many Matching The temporal nature of data. Structural similarity in rules. mammal(x)  feline(x)  carnivorous(x)  has- spots(x)  jaguar(x) mammal(x)  feline(x)  carnivorous(x)  has- stripes(x)  tiger(x)

21. 21 Matching with Variable Persistance of variable binding consistency. son(x,y)  son(y,z)  grandparent(x,z)

22. 22 Complex and Approximate Matching A Bit of Dialogue with 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 me come here. ELIZA:Your boyfriend made you come here.

23. 23 Complex and Approximate Matching Person:He says I’m depressed much of the time. ELIZA:I am sorry to hear you are 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?

24. 24 Complex and Approximate Matching Person:Perhaps I could learn to get along with my mother. ELIZA:Tell me more about your family.

25. 25 Complex and Approximate Matching Some ELIZA - like rules (X me Y)  (X you Y) (I remember Z)  (Why do remember X just now ?) (My {family-member} is Y)  (Who else in your family is Y) (X {family-member} Y)  (Tell me more about your family)

26. 26 Conflict Resolution Preferences based on rules  Rule order  Prefer special cases over more general ones Preferences based on objects  Prefer some objects to others  location in STM Preferences based on states

27. 27 6.5 Control Knowledge Syntax for a Control Rule Under conditions A and B, Rules that do {not} mention X { at all, in their left-hand side, in their right-hand side}

28. 28 Control Knowledge will { definitely be useless, probably be useless... probably be especially useful definitely be especially useful}

29. 29 Control Knowledge Knowledge about which paths are most likely to lead quickly to a goal state is often called search control knowledge. It can take many forms : 1.Knowledge about which states are more preferable to others 2.Knowledge about which rule to apply in a given situation 3.Knowledge about the other in which to pursue subgoals 4.Knowledge about useful sequences of rules to apply

30. 30 > End of Pertemuan 8 Good Luck