Kuliah Sistem Pakar Pertemuan V “Representasi Pengetahuan”

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Kuliah Sistem Pakar

Pertemuan V

“Representasi Pengetahuan”

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Tujuan Pembelajaran

 _{Mengerti perang proses RPL terhadap Rekayasa Pengetahuan}  _{Mengerti Representasi Pengetahuan, tipe-tupe}

 Mengetahui Tipe – Tipe Representasi Pengetahuan

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Proses Rekayasa Pengetahuan

(

Knowledge Engineering Process)

Validasi

Pengetahuan PengetahuanSumber

Representasi Pengetahuan Basis

Pengetahuan

Justifkasi Penjelasan

Inferensi

Akuisisi Pengetahuan

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Knowledge Representation



Knowledge Representation

is concerned with

storing large bodies of useful information in a

symbolic format.



Most commercial ES are

rule-based systems

where the information is stored as rules.



Frames may also be used to complement rule-based

systems.

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Tipe-tipe Pengetahuan berdasar

Sumber



Deep Knowledge

(formal knowledge)



Shallow /Surface Knowledge

(non formal knowledge)

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Penjelasan ………

 Deep Deep knowledge knowledge atauatau pengetahuan pengetahuan formal,formal, pengetahuan bersifat umum yang

pengetahuan bersifat umum yang terdapat dalam sumber terdapat dalam sumber pengetahuan tertentu (buku, jurnal, buletin ilmiah dsb)

pengetahuan tertentu (buku, jurnal, buletin ilmiah dsb)

dan dapat diterapkan dalam tugas maupun kondisi

berbeda.

 Shallow knowledge Shallow knowledge atauatau pengetahuan non formal, pengetahuan non formal, pengetahuan-pengetahuan praktis dalam bidang tertentu

pengetahuan-pengetahuan praktis dalam bidang tertentu

yang diperoleh seorang pakar pengalamannya pada

bidang dalam jangka waktu cukup lama.

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 Pengetahuan HeuristikPengetahuan Heuristik

 Pengetahuan ProseduralPengetahuan Prosedural

 Pengetahuan DeklaratifPengetahuan Deklaratif

Tipe-tipe Pengetahuan berdasar Cara

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Representasi Pengetahuan

 Propotional LogicPropotional Logic (logika proposional)(logika proposional)  Semantic NetworkSemantic Network (jaringan semantik)(jaringan semantik)  Script, List, Table, dan TreeScript, List, Table, dan Tree

 Object, Attribute, dan ValuesObject, Attribute, dan Values

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Representation in Logic and

Other Schemas



General form of any logical process



Inputs (Premises)



Premises used by the logical process to

create the output, consisting of

conclusions (inferences)



Facts known true can be used to derive

new facts that also must be true

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

Two Basic Forms of Computational Logic

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

Symbols represent propositions, premises or

conclusions

Statement: A = The mail carrier comes Monday

through Friday.

Statement: B = Today is Sunday.

Conclusion: C = The mail carrier will not come

today.



Propositional logic: limited in representing

real-world knowledge

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Propositional Logic

 A proposition is a statement that is either A proposition is a statement that is either truetrue or or

false

 Once known, it becomes a premise that can be used Once known, it becomes a premise that can be used

to derive new propositions or inferences

 Rules are used to determine the truth (T) or falsity Rules are used to determine the truth (T) or falsity

(F) of the new proposition

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Propotional Logic



Logic dapat digunakan untuk melakukan penalaran :

Contoh :

Pernyataan B = Hari ini hari Minggu

Kesimpulan C = Pak Pos tidak akan datang hari ini

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Predicate Calculus

 Predicate logic breaks a statement down into Predicate logic breaks a statement down into

component parts, an object, object characteristic or

some object assertion

 Predicate calculus uses variables and functions of Predicate calculus uses variables and functions of variables in a symbolic logic statement

variables in a symbolic logic statement

 Predicate calculus is the basis for Prolog Predicate calculus is the basis for Prolog (PROgramming in LOGic)

(PROgramming in LOGic)

 Prolog Statement ExamplesProlog Statement Examples

 comes_on(mail_carrier, monday).comes_on(mail_carrier, monday).  likes(jay, chocolate).likes(jay, chocolate).

(Note - the period “.” is part of the statement)

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

Merupakan gambaran pengetahuan

berbentuk grafs dan menunjukkan

hubungan antar berbagai obyek.



Obyek, berupa benda

atau

peristiwa



Nodes Obyek



Arc (Link) Keterhubungan

(Relationships)

_{(Relationships)}

*

* is a

is a

* has a

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Scripts

SCRIPT

,

skema skema representasi representasi pengetahuan pengetahuan yang yang menggambarkan urutan dari kejadian. Elemen-elemen

menggambarkan urutan dari kejadian. Elemen-elemen

script terdiri dari :

 Elements include Elements include

 Entry ConditionsEntry Conditions  PropsProps

 RolesRoles  Tracks Tracks  ScenesScenes

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 LIST,LIST,

daftar tertulis dari item-item yang saling

berhubungan.

 Umumnya digunakan untuk merepresentasikan Umumnya digunakan untuk merepresentasikan

hirarki pengetahuan dimana suatu obyek

dikelompokan, dikategorikan sesuai dengan

 Rank or Rank or

produk-produk dalam suatu katalog.

List

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 DECISION TABLE,DECISION TABLE, pengetahuan yang diatur dalam pengetahuan yang diatur dalam

format lembar kerja atau

format lembar kerja atau spreadsheetspreadsheet, menggunakan , menggunakan kolom dan baris.

kolom dan baris.

Attribute List

Conclusion List

Different attribute configurations are matched against

the conclusion

 Contoh :… ?Contoh :… ?

Decision Tabel

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Decision Trees

 DECISION TREEDECISION TREE,, _tree_tree_{yang berhubungan dengan}_{yang berhubungan dengan}_decision_decision table

table namun sering digunakan dalam analisis sistem komputer namun sering digunakan dalam analisis sistem komputer (bukan sistem AI).

(bukan sistem AI).

 Contoh :… ?Contoh :… ?

 Related to tables Related to tables

 Similar to decision trees in decision theorySimilar to decision trees in decision theory

 Can simplify the knowledge acquisition processCan simplify the knowledge acquisition process  Knowledge diagramming is frequently more Knowledge diagramming is frequently more

natural to experts than formal representation

methods

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Object, Attribute, Values

OBJECT

OBJECT : :

 OBJECTOBJECT dapat berupa fisik atau konsepsi. dapat berupa fisik atau konsepsi.

ATTRIBUTE

ATTRIBUTE : :

 ATTRIBUTEATTRIBUTE adalah karakteristik dari object. adalah karakteristik dari object.

VALUES

VALUES : :

 VALUESVALUES adalah ukuran spesifik dari attribute dalam adalah ukuran spesifik dari attribute dalam

situasi tertentu

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Object Attribute Values

_{Object Attribute Values}

Rumah

Rumah Kamar tidurKamar tidur 2,3,4, dsb.2,3,4, dsb.

Rumah

Rumah WarnaWarna Hijau, Putih, Hijau, Putih, Coklat dsb.

Coklat dsb.

Diterima di

Universitas

Universitas Nilai Ujian masuk

Nilai Ujian masuk A, B, C atau DA, B, C atau D

Pengendalian

persedian

persedian Level persediaan

Level persediaan 15, 20, 25, 35, 15, 20, 25, 35, dsb.

dsb.

Kamar tidur

Kamar tidur UkuranUkuran 3x4, 5x6, 4x5, 3x4, 5x6, 4x5, dsb.

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Production Rules

PRODUCTION RULES:



Production system dikembangkan oleh

Newell dan Simon sebagai model dari

kognisi manusia. Ide dasar dari sistem ini

adalah pengetahuan digambarkan sebagai

production rules dalam bentuk

pasangan

_pasangan

kondisi-aksi

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Production Rules

 Condition-Action PairsCondition-Action Pairs

 IF this condition (or premise or antecedent) IF this condition (or premise or antecedent)

occurs,

 THEN some action (or result, or conclusion, or THEN some action (or result, or conclusion, or

consequence) will (or should) occur

 IF the stop light is red AND you have stopped, IF the stop light is red AND you have stopped,

THEN a right turn is OK

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 Each production rule in a knowledge base represents Each production rule in a knowledge base represents

an

an autonomous chunkautonomous chunk of expertise of expertise

 When combined and fed to the inference engine, the When combined and fed to the inference engine, the

set of rules behaves synergistically

 Rules can be viewed as a simulation of the cognitive Rules can be viewed as a simulation of the cognitive

behavior of human experts

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Contoh : Production Rules



RULE 1 :

JIKA konfik internasional mulai

MAKA harga emas naik



RULE 2 :

JIKA laju infasi berkurang

MAKA harga emas turun



RULE 3

:

JIKA konfik internasional

berlangsung lebih dari tujuh

hari

dan

_dan

JIKA konfik terjadi di Timur

Tengah

MAKA beli emas

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Production Rules



Condition-Action Pairs



IF this condition (or premise or

antecedent) occurs,



THEN some action (or result, or

conclusion, or consequence) will (or

should) occur



IF the stop light is red AND you have

stopped, THEN a right turn is OK

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

Each production rule in a

knowledge base represents an

autonomous chunk

of expertise



When combined and fed to the

inference engine, the set of rules

behaves synergistically



Rules can be viewed as a

simulation of the cognitive

behavior of human experts



Rules represent a

model

of actual

human behavior

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Forms of Rules

 IF premise, THEN conclusionIF premise, THEN conclusion

 IF your income is high, IF your income is high,

 THEN your chance of being audited by the THEN your chance of being audited by the

IRS is high

 Conclusion, IF premiseConclusion, IF premise

 Your chance of being audited is high, IF Your chance of being audited is high, IF

your income is high

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 Inclusion of ELSEInclusion of ELSE

 IF your income is high, OR your deductions are IF your income is high, OR your deductions are

unusual, THEN your chance of being audited by

the IRS is high, OR ELSE your chance of being

audited is low

 More Complex RulesMore Complex Rules

 IF credit rating is high AND salary is more than IF credit rating is high AND salary is more than

$30,000, OR assets are more than $75,000, AND

pay history is not "poor," THEN approve a loan up

to $10,000, and list the loan in category "B.”

 Action part may have more information: THEN Action part may have more information: THEN

"approve the loan" and "refer to an agent"

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Frame

 FRAMEFRAME adalah struktur data yang berisi semua adalah struktur data yang berisi semua

pengetahuan tentang obyek tertentu. Pengetahuan

ini diatur dalam suatu struktur hirarkis khusus yang

memperbolehkan diagnosis terhadap independensi

pengetahuan. Frame pada dasarnya adalah aplikasi

dari pemrograman berorientasi objek untuk AI dan

ES.

 Setiap frame mendefinisikan satu objek, dan terdiri Setiap frame mendefinisikan satu objek, dan terdiri

dari dua elemen :

dari dua elemen : slotslot (menggambarkan rincian dan (menggambarkan rincian dan karakteristik obyek) dan

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Frames

 FrameFrame: Data structure that includes all the : Data structure that includes all the

knowledge about a particular object

 Knowledge organized in a hierarchy for diagnosis of Knowledge organized in a hierarchy for diagnosis of

knowledge independence

 Form of Form of object-oriented programmingobject-oriented programming for AI and ES. for AI and ES.  Each Frame Describes One ObjectEach Frame Describes One Object

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Contoh Frame

Automobile Frame

Class of : Transportation

Name of Manufacturer : Audi

Origin of Manufacturer : Germany

Model : 5000 turbo

Wheelbase : 105.8 inches

Number of doors : 4 (default)

Transmission : 3-speed (automatic)

Number of wheels : 4 (default)

Gas mileage : 22 mpg average (procedural attachment)

Engine Frame

Cylinder bore : 3.19 inches

Cylinder stroke : 3.4 inches

Compression ratio : 7.8 to 1

Fuel system : Injection with turbocharger

Horsepower : 140 hp

Torque : 160 ft/Lbs

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Hirarki Frame (exp : Vehicle)

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Advantages and Disadvantages of Different Knowledge Representations

Scheme Advantages Disadvantages

Production

rules Simple syntax, easy to understand, simple

interpreter, highly modular, flexible (easy to add to or modify)

Hard to follow hierarchies, inefficient for large systems, not all knowledge can be expressed as rules, poor at representing structured descriptive knowledge

Semantic

networks Easy to follow hierarchy, easy to trace associations, flexible

Meaning attached to nodes might be ambiguous,

exception handling is difficult, difficult to program

Frames Expressive power, easy to set up slots for new properties and relations, easy to create specialized procedures, easy to include default information and detect missing values

Difficult to program,

difficult for inference, lack of inexpensive software

Formal logic Facts asserted independently of use, assurance that all and only valid consequences are asserted (precision),