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INFO 629 Concepts in Artifcial Intelligence

Expert Systems

Fall 2004

Proessor! "r# $osina %e&er

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'ig(lig(ts Concept

Methodology Knowledge and reasoning

Knowledge representation

Forward, backward chaining ES and AI tasks

Maintenance

Knowledge acquisition

i!ited, bounded do!ains

"se o# shells

Ad$antages%disad$antages o# ES

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Expert Systems

Co!puter syste!s that can per#or!e&pert tasks'(general, $ague)

A !ethodology that !anipulatese&plicit knowledge with an in#erence

engine to per#or! AI tasks'

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t(e concept

knowledgebase

(e.g.,framesand methods)

knowledgebase


e&pertproble!

e&pertproble!

inferenceengine

(agenda)

inferenceengine

(agenda)e&pert

solution

e&pert

solution

)no*le+ge

reasoning

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e&pertsolution

e&pertsolution

*he co!plete !ethodology

knowledgebase


knowledgebase


explanation

explanation

general

knowledge

general

knowledge

userInt

erfac

e

userInterfac

e

e&pertproble!

e&pertproble!inference

engine(agenda)

inferenceengine

(agenda)

working memory(short-term mem/information)

working memory(short-term mem/information)

Knowledge acuisition

Knowledge acuisition

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Expert Systems

Knowledge

and

reasoning

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,no*le+ge representation ormalisms

(production) rules

#ra!es (concepts, ob+ects,#acts)

belie# networks

!ethods

ob+ectoriented se!antic nets

logic

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In#erence Engines

Forward chaining-Analysis, !ultiple outco!es

.ackward chaining-Atte!pt to test li!ited nu!ber o#hypotheses

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-aintenance

Maintenance #ocus on knowledge Co!ple&ity o# interrelations a!ong

rules

/i0cult to auto!ate !aintenance

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,no*le+ge ac./isition

Fro! se$eral hu!an e&perts- "nstructured inter$iews

- Structured inter$iews

- Methods learned #ro! psychology Auto!ated through !achine learning

!ethods

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"omains

i!ited, bounded do!ains

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ES S(ells

Easy prototyping to test ideas KA11A 1C

CI1S

E&a!ples in KA11A 1C

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ES an+ AI tas)s

Fro!2

/urkin, 3'(4556)'

E&pertSyste!s2design andde$elop!ent'1rentice7all,Inc', 8ew3ersey'

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a+antages 1i

1er!anence o# knowledge E&pert syste!s do not #orget orretire or quit, but hu!an e&perts !ay

.readth 9ne ES can (and should) entail knowledge learned#ro! an unli!ited nu!ber o# hu!an e&perts'

:eproducibility Many copies o# an e&pert syste! can be

!ade, but training new hu!an e&perts is ti!econsu!ing ande&pensi$e'

*i!eliness Fraud and%or errors can be pre$ented' In#or!ationis a$ailable sooner #or decision !aking

Entry barriers E&pert syste!s can help a ;r! create entry

barriers #or potential co!petitors

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a+antages 1ii

E0ciency can increase throughput and decreasepersonnel costs

Although e&pert syste!s !ay be e&pensi$e to build and!aintain, they are ine&pensi$e to operate

I# there is a !a

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a+antages 1iii

/ocu!entation An e&pert syste! can pro$ide per!anent

docu!entation o# the decision process Increased a$ailability2 the !ass production o# e&pertise

Co!pleteness An e&pert syste! can re$iew all thetransactions, a hu!an e&pert can only re$iew a sa!ple? an ESsolution will always be co!plete and deter!inistic

Consistency @ith e&pert syste!s si!ilar transactions handledin the sa!e way' 7u!ans are inuenced by recency e>ects andpri!acy e>ects (early in#or!ation do!inates the +udg!ent)'

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a+antages 1i

/i>erentiation In so!e cases, an e&pert syste! candi>erentiate a product or can be related to the #ocus o# the ;r!

:educed danger2 ES can be used in any en$iron!ent

:eliability2 ES will keep working properly regardless o# o#e&ternal conditions that !ay cause stress to hu!ans

E&planation2 ES can trace back their reasoning pro$iding

+usti;cation, increasing the con;dence that the correct decisionwas !ade

Indirect ad$antage is that the de$elop!ent o# an ES requiresthat knowledge and processes are $eri;ed #or correctness,co!pleteness, and consistency'

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+isa+antages

Co!!on sense In addition to a great deal o# technicalknowledge, hu!an e&perts ha$e co!!on sense' *o progra!co!!on sense in an ES, you !ust acquire and represent rules,which is not #easible'

Creati$ity 7u!an e&perts can respond creati$ely to unusualsituations, e&pert syste!s cannot'

earning 7u!an e&perts auto!atically adapt to changing

en$iron!ents? e&pert syste!s !ust be e&plicitly updated' Co!ple&ity and interrelations o# rules grow e&ponentially as

!ore rules are added'

Sensory E&perience 7u!an e&perts ha$e a$ailable to the! awide range o# sensory e&perience? e&pert syste!s are currently

dependent on sy!bolic input'

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+isa+antages 1ii

/egradation E&pert syste!s are not good at recogni

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Necessary gro/n+s or comp/ter/n+erstan+ing

Ability to represent knowledge andreason with it'

1ercei$e equi$alences and analogiesbetween two di>erent representations

o# the sa!e entity%situation' earning and reorgani

Documents

629ppt2