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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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