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Pertemuan 13Weak Slot-and-Filler Structures

Matakuliah : T0264/Inteligensia Semu Tahun : Juli 2006Versi : 2/2

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Learning Outcomes

Pada akhir pertemuan ini, diharapkan mahasiswa akan mampu :• << TIK-99 >>• << TIK-99>>

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Outline Materi

• Materi 1• Materi 2• Materi 3• Materi 4• Materi 5

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9.2. Frames

• Frame (Bingkai).Frame merupakan kumpulan pengetahuan tentang suatu obyek tertentu, peristiwa, lokasi, situasi atau informasi lainnya.

• Frame memiliki slot yang menggambarkan rincian (atribut) dan karakteristik obyek.

• Frame biasanya digunakan untuk merepresentasikan pengetahuan yang didasarkan pada karakteristik yang sudah dikenal yang merupakan pengalaman-pengalaman.

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Frames

• Dengan menggunakan Frame maka sangat mudah untuk membuat inferensi tentang obyek, peristiwa atau situasi baru. Hal ini karena Frame menyediakan basis pengetahuan yang ditarik dari pengalaman.

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Frames : Transportasi Darat

Macam Mobil

Mobil sedan

Mobil minibus

Alat Transportasi

Transp. Laut

Transp. Darat

Macam Angk Darat

Angk. Tanpa mesin

Slot Mobil

Jenis BBM

Mobil Bensi

Mobi Solar

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Frames

A Simplified Frame SystemPerson

isa : Mammalcardinality : 6,000,000.000* handed : Right

Adult-Maleisa : Personcardinality : 2,000,000,000*height : 5-10

ML-Baseball-PlayerIsa : Adult-MaleCardinality : 624*height : 6-1*bats : equal to handed*batting-average : .252*team :*uniform-color :

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Representing the Class of All Teams as a MetaclassClass

instance: Classisa : Class*cardinality :

Teaminstance: Classisa : Classcardinality :{the number of teams that exist}*team size : {each team has a size}

ML-Baseball-Teaminstance: Classisa : Classcardinality :26 {the number of baseball team that exist}*team size : 24 {default 24 players on team}*manager

Brooklyn-Dodgersinstance: ML-Baseball-Teamisa : ML-Basball-Plyerteam size : 24 manager : Lea-Durocher*uniform-color : Blue

Pee-Wee-Reeseinstance: Brooklyn-Dodgersisa : Fielderuniform color : Blue batting-average : .309

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Classes and Metaclasses

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Representing Relationships among Classes

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Representing Relationships among Classes

ML-Baseball-Playeris-covered-by : {Pitcher, Catcher, Fielder}

{American-Leaguer, National-Leaguer}Pitcher

isa : ML-Baseball-Playermutually-disjoint-with : {Catcher, Fielder}

Catcher isa : ML-Baseball-Playermutually-disjoint-with : {Pitcher, Fielder}

Fielder isa : ML-Baseball-Playermutually-disjoint-with : {Pitcher, Catcher}

American-Leaguer isa : ML-Baseball-Playermutually-disjoint-with : {National-Leaguer}

National-Leaguerisa : ML-Baseball-Playermutually-disjoint-with : {American-Leaguer}

Three-Finger-Browninstance : Pitcherinstance : National-Leaguer

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Slots as Full-Fledged Objects

We want to be able to represent and use the following properties of slots (attributes or relations) :

•The classes to which the attribute can be attached.

•Constraints on either the type or the value of the attribute.

•A value that all instances of a class must have by the definition of the class.

•A default value for the attribute.

•Rules for inheriting values for the attribute.

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Slots as Full-Fledged Objects

• Rules for computing a value separately from inheritance.

• An inverse attribute.

• Whether the slots is single-valued or multivalued.

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Representing Slots as Frames, I

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Representing Slots as Frame, II

Slots

isa : Classinstance : Class•domain : •range :•range-constraint :•definition :•default :•transfers-through :•to-compute :•inverse :•single-valued :

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Representing Slots as Frame, II

manager

instance :slotsdomain :ML-Baseball-Teamrange :Personrange-constraint : x(experience x.manager)default :inverse : manager-ofsingle-valued: TRUE

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Representing Slots as Frames, IIImy-manager

Instance :Slot Domain :ML-Baseball-Player Range :Person Range-constraint :x(experience x.my-manager) To-compute :x (x.team).manager Single-valued :TRUE

Color

Instance :Slot Domain :Physical-Object Range :Color-Set Transfers-through :Top-level-part-of Visual-salience :High Single-valued :FALSE

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Representing Slots as Frames, IVUniform-color

Instance :Slot Isa :color Domain :Team-player Range :Color-Set Range-constraint :not Pink Visual-salience :High Single-valued :FALSE

Bats

Instance :Slot Domain :ML-Baseball-Player Range :{Left, Right, Switch} To-compute :x x.handed Single-valued :TRUE

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Tangled HierarchiesHierarchies that are not trees are called tangled hierarchies

We want to decide whether Fifi can fly

The correct answer is no

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Tangled Hierarchies

Determining whether Dick is a pacifist.

Transverse multiple instance link and more than one answer can be found along the path.

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More Tangled Hierarchies

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More Tangled Hierarchies

• In the case of (a), our new algorithm reaches Bird (via Pet-Bird) before it reaches Ostrich. So it report that Fifi can fly.

• In the case (b), the algorithm reaches Quaker and stops without noticing a contradiction.

• The problem is that path length does not always corresponds to the level of generality of class.

• The solution to this problem is to base our inheritance algorithm not on path length but on the notion of inferential distance, which can be defined as follows :

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Defining Property InheritanceInferential Distance :

Class1 is closer to Class2 than to Class3 if and only if Class1 has an inference path through Class2 to Class3 (in other words, Class2 is between Class1 and Class3)

We can now define the result of inheritance as follows : The set of competing values for a slot S in a frame F contains all those values that

• Can be derived from some frame X that is above F in the isa hierarchy• Are not contradicted by some frame Y that has a shorter inferential distance to F than X does

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Algorithm : Property Inheritance

To retrieve a value V for slot S of an instance F do : 1. Set CANDIDATES to empty.2. Do breadth-first or Dept-first search up the isa

hierarchy from F, following all instance and isa link. At each step, see if a value for S or one of its generalizations is stored.

a. If a value is found, add it to CANDIDATES and terminate that branch of the search.

b. If no value is found but there instance or isa link upward, follow them.

c. Otherwise terminate the branch.

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Algorithm : Property Inheritance

3. For each element C of CANDIDATES do : a. See if there is any other element of

CANDIDATES that was derived from a class closer to F than the class from which C came.

b. If there is, then removed C from CANDIDATES.

4. Check the cardinality of CANDIDATES : a. If it is 0, then report that no value was found.b. If it is 1, then return the single element of

CANDIDATES as V.c. If it is greater then 1, report a contradiction.

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<< CLOSING>>

End of Pertemuan 13

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