chp02_3_ExpertSystem

8/7/2019 chp02_3_ExpertSystem

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1

Introduction to Artificial

Intelligence

EXPERT SYSTEM


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From the Father of Expert System:

An intelligent program that usesknowledge and reasoning procedureto solve problems that requiresignificant human expertise for their

solutions.

- Edward Feigenbaum

Definition


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Definition

A syste m th at u se s h u m an K cap tu red in aco m p u te r to so lv e p ro b le m s th a t o rd in a rily

re q u ire h u m a n e xp e rtise

&T u rb a n A ro n so n( )2001

A com p u te r p rog ra m th at re p re se n ts an dre a so n s w ith K o f so m e sp e cia list su b je ct

w ith a v ie w to so lvin g p ro b le m o r g iv in ga d v ice

( )Jackson 1 99 9

A com p u ter p rog ram d esig n ed to m od el th e-

p ro b le m solvin g a b ility o f a h u m a n e x p e rt

( )D u rkin 1 9 9 4

A com p u te r p rog ra m th at e m u la te s th ere a so n in g o f h u m a n e xp e rts in a p ro b le m

d o m a in

( )A w a d 1 9 9 6

:Fro m o th e rs


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As a field, It is a branch of AI

As a technology,

It is the most widely applied AI technology Among the first to be commercialized

As an application, It is a computer program It transfers (i.e. it acquires and represents)

practical knowledge (i.e. expertise/rules ofthumb/heuristic) from human expert tocomputer

Some facts about ES


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It reasons (or it thinks) with what ittransfers

It can either support decision makers (by

recommending decisions) or

replace them (by making decisionson behalf of experts, releasingthem from routine tasks).

3.

Some other facts aboutES


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After all,3.

Some other facts aboutES

A n is o n e w h o

,p o sse sse s sp e cia lize d skill,exp e rie n ce a n d kn ow le d g e

th a t m o st p e o p le d o n o th a v e a lo n g w ith th e a b ility

to a p p ly th is k n o w le d g e, ,u sin g tricks sh o rtcu ts a n d

- -ru le s o f th u m b to re solve a.p ro b le m e fficie n tly

& ( )H a rm o n K in g 1 9 8 5

S replicates a humanexpert .expert


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Therefore, ES possess expertise is

an extensive, task-specific knowledge held

by experts hard to capture. Capturing it is a major

issue in ES development, and became amajor concern of Knowledge Acquisitionresearchers.

With the expertise stored in its knowledge base,ES can provide expertise-based solutionswhich are imaginative, accurate and efficient.

o m e o th e r fa cts a b o u tE SE xp e rtise


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E xp e rt p e rso n n e l is a va lu a b le a sse t fo r(a n y o rg a n iza tio n a s th e y a re g o o d a t

,so lv in g o rg a n iza tio n a l p ro b le m s p la n n in g.) e tc ye t th e y a re

Pe rish a b le a n d irre p la ce a b le G e o g ra p h ica lly sta tic /N o t a v a ila b le 2 4 7 E m o tio n a lly a ffe cte d

, fe e l fe a r stre ss e tc like o th e r h u m a n.b e in g s

C o stly to tra in a n d to co n su lt

.. and some facts about anexpert


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So, we have good reasons to build an ES!!

3 main reasons:To replace human expertTo assist human expert

To gain competitive advantage

Reasons for building ES


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To replace to eliminate

Reasons for replacing experts

To preserve their expertise To disseminate their expertise in lessexpensive manner

To make expertise available after hours To make expertise available at several

locations To free experts from routine thus they canfocus on the other critical tasks

To avoid experts from danger

Reason 1: To replaceexpert


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ES as an aided tool to improve human experts productivity

maintain consistency in their

decisions deal with the complexity of the tasks

make available the information that is

difficult for experts to recall

Reason 2: To assistexperts


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Due to the benefits this technologycan offer

Exemplar:

Digital Equipment Corporation R1/XCON

American Express

Authorizers Assistant

Coopers & Lybrand

ExperTax

Reason 3: To gaincompetitive advantage


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ES Component

nowledge base

orking memory

nference engineUser

Interface


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-o n g te rm m e m o ry SENSO -h o rt te rm m e m o ry

B ra in

T h e co m p o n e n ts a ctu a lly m im ic w h a t is in.h u m a n s

in comparison withhumans

=

K no w led ge ba se

=

in

ter

face

= Inference engine

=W o rkin g m e m o ry

E n v iro n m e n t=

/Pe o p le sen sor e tc

th a t p ro vid e in p u tto o u r b ra in


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Knowledge base contains the domainknowledge Facts

Heuristics or rules that direct use ofknowledge to solve specific problems in aparticular domain.

Typical representation: Rules (IF x AND y THEN z @ x y z )

Example (for predicting weather): IF cloudy = yes AND temperature = low AND

humidity = high THEN it will rain.

IF cloudy = no AND temperature = high AND

humidity = lowTHEN it will sunny

Component 1:Knowledge Base


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A storage area for current data i.e. facts entered by user during consultation

with ES (e.g. symptoms of a disease)

Input data can also be loaded fromexternal storage such as databases,spreadsheets or sensors.

Also a place where intermediate

conclusions or the new facts inferred byES are stored

Non-permanent content will be deleted

when the session ends.

Component 2: WorkingMemory


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Known as rule interpreter in rule-basedES.

Is modelled after human experts

reasoning.Typically, inference engine utilized 2

control strategies: Backward Chaining (goal driven)

determine fact in the conclusion to prove theconclusion is true.

Forward Chaining (data driven) premise clause match situation then assert

conclusion.

Component 3: InferenceEngine


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Facilitates all communication between user and ES.

Communication are in natural language style, interactiveand follow closely the conversation between humans.

Two types of interaction: ES ask for information through questions, provide

the results and display the explanation.

User supply answers, receive the results or query

the system (i.e. getting explanation)

Component 4: UserInterface


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Two types of explanation:

WHY Explain why the system asked the question.

HOW Explain how ES arrived at the conclusion.

Justify the validity of the systems findings

increase user confidence and trust

Component 5: Explanationsub-system


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ES Development Process

EXPERT PROJECTMANAGER

K SYSTEMDEVELOPER

ENGINEER/ANALYST

K MANAGER

USER

KS

manages

managesuses

designs &

implements

validates

elicits knowledge from

elicitsrequirements

from

deliversanalysis models to

defines K strategyinitiates K development projects

facilitates K distribution

:ou rc e S ch re ib er t a l . ( )000


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Knowledge engineering Methodology for building an ES

6 phases of knowledge engineering: Problem assessment

Knowledge acquisition

Design

Testing Documentation

Maintenance



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Choice oftools and approaches for developingES includes: Programming languages Support aids and tools Ready-to-use customized packages for

industry and government

ES shells

Which tools to adopt depends on: The nature of the problem The skill of the builder The function ES is expected to perform

(either diagnoses or monitoring)

ES Development Tool


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Main player in ESDevelopment

D o m a in ex p e rt Pro v id e kn o w le d g e o r m e th o d to solve

p ro b le m

K n ow led g e E n g in ee r G a in kn o w le d g e fro m ex p e rt /Tra n sfe r re p re sen t kn o w le d g e

in to a co m p u te r

U se r C an b e the en d

u se r or exp e rth im se lf


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ES has been applied to perform/solve thefollowing task/problem Control meeting certain

standards/specifications

Design configuring objects under specificconstraints

Diagnosis inferring malfunction/diseaseand recommend solutions/treatment

Planning designing actions

Monitoring comparing observation toexpectation

Selection identifying the best choice(s)from a list of actions

Task/Paradigm


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ES has been applied to perform/solve thefollowing task/problem Interpretation infer situation description

from observation

Prediction infer likely consequences of thegiven situation

Debugging prescribe remedies formalfunction

Repair execute a plan to administer aprescribed remedy

Instruction diagnose, debug and correctstudents misconception

Task/Paradigm


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Benefits & Limitations

B E N EFITS LIM IT A T IO N S

R e d u ce d e cisio n m a kin g tim e W o rk w e llo n ly w ith in a n a rro wd om ain of kn ow led g eIm p ro v e p ro d u ctio n o p e ra tio n s C a n m a ke m ista ke s

In cre a se o u tp u t a n dp ro d u ctiv ity

R isk o f kn o w le d g e q u icklyb e com e o b sole teC a n b e u se d a s to o ls fo r sta fftra in in g

O n g o in g re lia n ce o n e x p e rts

R e te n tio n o f sca rce e xp e rtise K n o w le d g e is n o t a lw a ysa v a ila b leU p g rad e p e rfo rm an ce D ifficu lt to e xtra ct e x p e rtise

from h u m a n e xp e rtsR ela tiv e ly a ffo rd a b le e x p e rtise U ser la ck o f tru st ca n im p e d eu seIm p ro v e q u a lity o f

/p ro d u cts se rvice s


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In general, ES works by matching thefacts with its knowledge base content,and display the output to user

ES .. How it works?

nowledge base

orkingmemory

nference engineUser

Int

er

face

A

:12 A BModus !onens

B


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In detail, it depends on what controlstrategy each ESs inference engineutilizes, either forward chaining or

backward chaining or both The principle of chaining is governed bymodus ponens.

A B C

A B C

Chaining signifies linking of a set of

pertinent rules.

ES .. How it works?

S i


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Goal rule A rule in which its conclusion is not a

premise of any other rules in theknowledge base

E.g. R1: (A B) C D R2: D G T

R3: P Q B

R2 is the goal rule as its conclusion,T, isNOT one of the premises of the other rule

(i.e. R1 and R3)

Some importantconcepts

S i


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Sub-goal rule A rule in which its conclusion is also a

premise of the other rules or in the goalrule.

E.g. R1: (A B) C D R2: D G T R3: P Q B

R1 and R3are sub-goal rule as theirconclusions are premises of the other rules

Conclusion of R1, i.e. D, is a premise ofR2

Conclusion of R3, i.e. B, is a premise of


S i t t


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Primitive premise A premise that is not a conclusion of any

other rules

E.g.

R1: (A B) C D R2: D G T R3: P Q B

A, C, G, P and Qare primitive premises.WHY? Look at the THEN part of R1, R2and R3

None of these rules has either A or C or G or P orQ as a conclusion.


S i t t


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Non-primitive premise A premise that is also a conclusion of the

other rule(s)

E.g. R1: (A B) C D

R2: D G T R3: P Q B

B and D are non-primitive premises. WHY? Look at the IF part of R1 and R3. R1 has D as a conclusion while R3 has B as a

conclusion.

Both B and D are premises, at the same time

they also are conclusions, thus they are non-


S i t t


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Rule fire A rule fire means rule is concluded. In

other words, it refers to a state where theconclusion of that rule is proved as true,because its premise(s) is true

E.g. R1: (A B) C D

If A and B are true, or if C is true, thenwe say R1 fire with a conclusion Dtrue.

Rule not fire Is a vice versa of rule fire due to its



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Backward chaining overview An Inference strategy that attempts to prove

a hypothesis by gathering supportinginformation

The system works from the goal by chainingrules together to reach a conclusion orachieve a goal

In other words, it start with the goal, andthen looks for all relevant, supportingpremises that lead to achieving the goal.

Backward Chaining


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B k d Ch i i


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With example: R1: (A and B) or C implies D R2: D and G implies T .. goal rule

1.2.Identify the goal . T.

3.Identify the goal rule .. R2.

4.Check R2s first premise, i.e. D

D is non-primitive . it belongs to R1 asconclusion, so jump to R1.

Check R1s premises.

A is primitive. If it is in working memory,continue checking B. If not, ASK user a

question. If the answer is yes, continuechecking B. If no, check C.

Backward ChainingSteps

B k d Ch i i


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4.Either A and B are true or C is true, fire R1.

5.Jump back to R2.

6.Repeat 3 with R2s second premise, i.e. G.

G is primitive. If it is in working memory,fire R2. Otherwise, R2 fail to fire andtherefore, the goal cannot be proven.

7.End of step.

Backward ChainingSteps


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Forward Chaining overview An Inference strategy that begins with a set of

known facts, derives new facts using ruleswhich premises match the known facts,

continues until goal reached or no morerules matches.

Begins with known data and works forward to

see if any conclusions (new information)can be drawn.

Forward Chaining


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1.Get initial data and place it in working memory.2.Scan the rules searching for matched premises.3.If found

fire the rule add its conclusion to working memory.

4.Repeat Steps 2 & 3 until no more match or goal isachieved.

Forward Chaining Steps


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With example: R1: (A and B) or C implies D

R2: D or G implies T

1.Get initial data.

2.Scan the rules in sequence. If A and B are true, or C is true, R1 fires and D is

inserted into working memory. D will cause R2to fire when R2 is scanned. Process is then

terminated as all rules have been scanned, andno more match can be done.

If none of A, B and C true, continue scan the nextrule, i.e. R2. If G is true, R2 fires and T isinserted into working memory. Process is thenterminated as all rules have been scanned, and

no more match can be done.



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What if T is in R1? R1: (A and B) or T implies D

R2: H or G implies T

1.Get initial data.

2.Scan the rules in sequence. If none of A, B andT is true, scanning is continued

with R2.

If H or G is true, R2 fires andT is inserted into

working memory. End of 1st

cycle withconclusionT.

The 2ndcycle of scanning and firing rules begins. Tis now in working memory, therefore R1 fires. Dis concluded and inserted into working memory.

End of 2

nd

cycle with conclusions T and D. Themost recent, i.e. D, becomes the final



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Conflict resolution A process to determine which rule to fire

(when the contents of the WM can cause>1 rule to fire)

Resolution strategy: Establish the goal and stop the system when the

goal is attained

The order of the rules that conclude the goal is

important (the engine will fire the first onelocated).

Assign rules with the priority values (reflect rulepreferences)

The system scans the rules, determines the rulesto fire, and fire the ones with highest priority.

Conflict Resolution

Backward vs Forward


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Attribute Backward Chaining Forward Chaining

Also known as Goal-driven Data-driven

Starts from Possible conclusion New data

Processing Efficient Somewhat wasteful

Aims for Necessary data Any conclusion (s)

Approach Conservative/cautious Opportunistic

Practical if Number of possible finalanswers is reasonable or aset of known alternatives is

available

Combinatorial explosioncreates an infinite number ofpossible right answers

Appropriate for Diagnostic application Scheduling and monitoring

Example ofapplication

Selecting a specific type ofinvestment

Making changes to corporatepension fund

Backward vs ForwardChaining

Documents

chp02_3_ExpertSystem