Matakuliah
Tahun
Versi
: T0264/Inteligensia Semu
: 2005
: 1/0
Pertemuan 5
Knowledge Representation Issues
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Learning Outcomes
Pada akhir pertemuan ini, diharapkan mahasiswa
akan mampu :
• << TIK-99 >>
• << TIK-99>>
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Outline Materi
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Materi 1
Materi 2
Materi 3
Materi 4
Materi 5
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4.1 Representations and Mappings
Mapping between Facts and Representation
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Three Representation of a Mutilated Checkerboard
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Representation of Facts
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4.2 Approaches to Knowledge Representation
Properties of a Representation System
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Representation adequacy
Inferential adequacy
Inferential efficiency
Acquisitional efficiency
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Inheritable Knowledge
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Viewing a Node as a Frame
Baseball-Player
Isa
bats
height
batting-average
: Adult-Male
:(EQUAL handed)
: 6-1
: .252
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4.3 Issues in Knowledge Representation
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Are there any basic attributes of objects ?
Are there any basic relationship among objects ?
At what level should knowledge be represented ?
How should sets be representation ?
How should knowledge be accessed ?
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4.3.1 Important Attributes
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4.3.2 Relationships among Attributes
Representing Inverses
• Reversible representations that ignore focus :
Team(Pee-Wee- Reese, Brooklyn-Dodgers)
• Pairs of focused entities :
- one associated with Pee Wee Reese :
team = Brooklyn-Dodgers
- one associated with Brooklyn Dodgers :
team-members = Pee-Wee-Reese,...
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4.3.3 Choosing the Granularity of Representation
Suppose we are interested in the following fact :
John spotted Sue.
We could represent this as
spotted(agent(John),object(Sue))
Questions :
Who spotted Sue ?
Did John see Sue ?
We could add facts, e.g.:
spotted(x,y)  saw(x,y)
An alternative representation :
saw(agent(John),object(Sue),timespan(briefly))
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Are There Primitive Concepts ?
Marry is Sue ‘s cousin.
• Marry = daughter(brother(mother(Sue)))
• Marry = daughter(sister(mother(Sue)))
• Marry = daughter(brother(father(Sue)))
• Marry = daughter(sister(father(Sue)))
An alternative :
• Marry = child(sibling(parent(Sue)))
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4.3.4 Representing Sets of Objects
Intensional and Extensional
Representation
Extensional :
{Earth}
Intensional :
{x : sun-planet(x)  human-inhabited(x)}
{x : sun-planet(x)  nth-farthest-from-sun(x,3)}
{x : sun-planet(x)  nth-biggest(x,5) }
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4.3.5 Finding the Right Structures as Needed
Selecting an Initial Structure
• Index structures by English words
- John flew to New York.
He rode in a plane from one place to another.
- John flew a kite.
* He held a kite that was up in the air.
- John flew down the street.
* He moved very rapidly.
- John flew into a rage.
An idiom
• Index structures by concepts
• Use one major clue
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4.4 The Frame Problem
A Similarity Net
BENCH
CHAIR
no back,too wide
too high, no back
too big, no back
TABLE
STOOL
drawers
DESK
SIDEBOARD
no knee room
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<< CLOSING>>
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