1 chapter 3 knowledge representation. 344-471 ai & eschapter 3 2 knowledge representation...

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Chapter 3

Knowledge Representation

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Knowledge Representation

Semantic Network Network Representation Conceptual Graph Frame

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Semantic Network

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Network Representation

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Conceptual Graph

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Conceptual Graph

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Case Frame

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Conceptual / Graph Frame : Hotel Bed

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Frame : Hotel Bed

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Frame : Bird

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Frame :Penguin 1 : Ambiguity

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Frame :Penguin 2 : resolve ambiguity

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Frame : Penguin 3 : Subclass relation

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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. เมรี่��เป็�นหลานของซู�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)))

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Knowledge RepresentationJohn 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.

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Inheritable Knowledge

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Frame

Baseball-Player

isa : Adult-Male

bats: (EQUAL handed)

height: 6-1

batting-average: .252

figure 4.7

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Procedural Knowledge as Rules

similar to figure 4.7

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Redundant Representation

John punched Mary

Mary punched John

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Similarity Net liking among set of frame

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Representations

man(Marcus)

man(Caesar)

person(Cleopatra)

X : man(X) person(X)

y person(y)

y = Cleopatra

y = Marcus

y = Caesar

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Matching

1. Indexing : problems:

1. need large number of rules

2. the precondition of rule is not always obvious

2. Matching with variablesproblems:

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

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Indexing

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Logic Programmingx : 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

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Complex Indexingtheir precondition approximately matchapproximately match the current situation -> ELIZAPerson: 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

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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

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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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