AI Ch8

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Ch8Expert System

Dr. Bernard Chen Ph.D.University of Central Arkansas

Spring 2!!

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"#tline Expert System introd#$tion

%#le&Based Expert System 'oal Driven Approa$h

Data Driven Approa$h

(odel&Based Expert System

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Expert System

)ntrod#$tion *#man experts are a+le to perform at a

s#$$essf#l level +e$a#se they kno, a lot a+o#ttheir areas of expertise

An Expert System#se kno,ledge spe$i-$ toa pro+lem domain to provide expert /#ality0performan$e in that appli$ation area

As ,ith skilled h#mans1 expert systems tend to+e spe$ialists1 fo$#sing on a narro, set ofpro+lems

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Expert System

)ntrod#$tion Be$a#se of their he#risti$1

kno,ledge intensive nat#re1 expert

systems generally S#pport inspe$tion of their reasoning

pro$esses Allo, easy modi-$ation in adding and

deleting skills from kno,ledge +ase %eason he#risti$ally1 #sing kno,ledge

to get #sef#l sol#tions

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Expert System

)ntrod#$tion Expert systems are +#ilt to solve a ,ide

range of pro+lems in domain s#$h as

medi$ine1 math1 engineering1 $hemistry1geology1 $omp#ter s$ien$e1 +#siness1 lo,1defense and ed#$ation

3hese programs address a variety ofpro+lems1 the follo,ing list is a s#mmary ofgeneral expert system pro+lem $ategories

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Expert System

)ntrod#$tion )nterpretation &&& forming high&level

$on$l#sions from $olle$tions of ra, data

Predi$tion &&& pro4e$ting pro+a+le$onse/#en$es of given sit#ations

Diagnosis &&& determining the $a#se ofmalf#n$tions +ased on o+serva+lesymptoms

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Expert System

)ntrod#$tion Design &&& -nding a $on-g#ration of

system $omponents that meets

performan$e goals ,hile satisfying a setof design $onstrains

Planning &&& devising a se/#en$e ofa$tions that ,ill a$hieve a set of goalsgiven starting $onditions and r#ntime$onstrains

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3he Design of %#le&Based

Expert System5 ar$hite$t#re of a typi$al expert

system for a parti$#lar pro+lem

domain.

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Expert System 3he hear of the expert system is the

kno,ledge +ase1 ,hi$h $ontains thekno,ledge of a parti$#lar appli$ation

domain

)n a r#le&+ased expert system1 thiskno,ledge is most often represented in

the form of ifthen

)n the -g#re1 the kno,ledge +ase $ontains+oth general and $ase&spe$i-$ information

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Expert System 3he inferen$e engine applies the kno,ledge to

the sol#tion of a$t#al pro+lems

)t is important to maintain this separation ofthe kno,ledge and inferen$e engine +e$a#se (akes it possi+le to represent kno,ledge in a more

nat#ral fashion Expert system +#ilder $an fo$#s on $apt#ring and

organi6ing pro+lem&solving kno,ledge than thedetails of $ode implementation Allo, $hange to +e made easily Allo,s the same $ontrol and interfa$e soft,are to +e

#sed in di7erent systems

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Sele$ting a pro+lem Expert System involve a $onsidera+le

investment of money and h#man e7ort

%esear$hers have developed g#idelinesto determine ,hether a pro+lem isappropriate for expert system sol#tion

3he need for the sol#tion 4#sti-es the $ostand e7orts of +#ilding an expert system

*#man expertise is not availa+le in allsit#ation ,here it is needed

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Sele$ting a pro+lem3he pro+lem domain is ,ell

str#$t#red and does not re/#ire

$ommon sense reasoning3he pro+lem may not +e solved #sing

traditional $omp#ting methods

Cooperative and arti$#late expertsexist

3he pro+lem is proper si6e and s$ope

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ASA Example ASA has s#pported its presen$e in spa$e +y

developing a 9eet of intelligent spa$e pro+esthat a#tonomo#sly explore the solar system

3o a$hieve s#$$ess thro#gh years in theharsh $onditions of spa$e travel1 a $raftneeds to +e a+le to radi$ally re$on-g#re its

$ontrol regimein response to fail#res andthen plan aro#nd these fail#res d#ring itremaining 9ight

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ASA Example :inally1 ASA expe$ts that the set of

potential fail#re s$enarios and possi+le

responses ,ill +e m#$h too large to#sesoft,are that s#pports pre9ighten#meration of all $ontingen$ies

Livingstone is an implemented kernel fora model&+ased rea$tive self&$on-g#ringa#tonomo#s system

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ASA Example A long&held vision of model&+ased reasoning

has +een to #se a single $entrali6ed model tos#pport a variety of engineering tasks

3he tasks in$l#de keeping&tra$k of developing plans Con-rming hard,are modes

%e$on-g#ring hard,are Dete$ting anomalies Diagnosis :a#lt re$overy

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ASA Example

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ASA Example

)t $onsist of a heli#m tank

%eg#lators

Propellant tanks

A pair of main engine

;at$h valves Pyro valves

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ASA Example

3he heli#m tank press#ri6es the t,o propellant tanks1,ith the reg#lators a$ting to red#$e the high heli#mpress#re

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ASA Example

3hr#st $an +e provided +y either ofthe main engines and there are a

n#m+er of ,ays of openingpropellant paths to either mainengine

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ASA Example

S#ppose the main engine s#+system has +een$on-g#red to provide thr#st from the leftengine +y opening the lat$h valves leading to it

And s#ppose this engine fails =overheating>1 sothat is fails to provide the re/#ired thr#st

3o ens#re that the desire thr#st is provided1 thespa$e$raft m#st +e transitioned to a ne,$on-g#ration in ,hi$h thr#st is no, provided+y the main engine on the right side

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Sele$ting a pro+lem

3he primary people involved in +#ilding anexpert system are the knowledge

engineer1domain expert1 andend user

3he domain expert is primarily responsi+lefor spelling o#t skills to kno,ledge

engineer )t is often #sef#l for kno,ledge engineer to

+e a novi$e in the pro+lem domain

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Exploratory development$y$le

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Exploratory development$y$le

)t is also #nderstood that the prototypemay +e thro,n a,ay if it +e$omes to

$#m+ersome or if the designers de$ideto $hange their +asi$ approa$h to thepro+lem

Another ma4or feat#re of expert systemis that the program need never +e$onsidered -nished0

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"#tline

Expert System introd#$tion




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Strategies for state spa$esear$h

)n data drivensear$h1 also $alled forward$haining1 the pro+lem solver +egins ,ith thegiven fa$ts of the pro+lem and set of legal

moves for $hanging state 3his pro$ess $ontin#es #ntil =,e hope??> it

generates a path that satis-es the goal$ondition

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ti$&ta$&toe0state spa$egraph

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Strategies for state spa$esear$h

An alternative approa$h (Goal Driven)is start ,ith thegoal that ,e ,ant to solve

See ,hat r#les $an generate this goal and determine,hat $onditions m#st +e tr#e to #se them

3hese $onditions +e$ome the ne, goals it ,ork +a$k to

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%#le&Based Expert System

%#le +ased expert system representpro+lem&solving kno,ledge as ifthen

)t is one of the oldest te$hni/#es forrepresenting domain kno,ledge in anexpert system

)t is also one of the most nat#ral and,idely #sed in pra$ti$al andexperimental expert system

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A #nreal Expert SystemExample%#le ! if

the engine is getting gas1 andthe engine ,ill t#rn over1then

the pro+lem is spark pl#gs.%#le 2 if

the engine does not t#rn over1 andthe lights do not $ome onthenthe pro+lem is +attery or $a+les.

%#le if the engine does not t#rn over1 andthe lights do $ome on

then

the pro+lem is the starter motor.%#le if

there is gas in the f#el tank1 and

there is gas in the $ar+#retorthenthe engine is getting gas.

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3he prod#$tion system at the start of a$ons#ltation in the $ar diagnosti$example.

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3he prod#$tion system at the start of a$ons#ltation in the $ar diagnosti$example.

3hree r#les mat$h ,ith this expression in,orking memory r#le !1 21 and

)f ,e resolve $on9i$ts in favor of thelo,est&n#m+ered r#le1 then r#le ! ,ill -re

3his $a#se to +e +o#nd to the val#espark pl#gs and the premises of r#le ! to+e pla$ed in the ,orking memory

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3he prod#$tion systemafter %#le ! has -red.

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3he prod#$tion systemafter %#le ! has -red.

ote that there are t,o premises to r#le!1 +oth of ,hi$h m#st +e satis-ed toprove the $on$l#sion tr#e

So no, ,e need to -nd o#t ,hether 3he engine is getting gas1 and 3he engine ,ill t#rn over

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3he system after %#le has -red. otethe sta$k&+ased approa$h to goalred#$tion.

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3he andor graph sear$hed in the$ardiagnosis example1 ,ith the $on$l#sion of

%#le mat$hing the -rst premise of %#le!.

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Explanation and3ransparen$y in 'oal&Driven %easoning 3he follo,ing dialog#e +egins ,ith the

$omp#ter asking the #ser a+o#t the

goals present in the ,orking memory 'as in f#el tank

YES

'as in $ar+#retor

YES Engine ,ill t#rn over

WHY

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Explanation and3ransparen$y in 'oal&Driven %easoning )n general1 the t,o /#estions ans,ered +y

r#le&+ased expert system are

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Explanation and3ransparen$y in 'oal&Driven %easoning 3he follo,ing dialog#e +egins ,ith the $omp#ter asking the #ser a+o#t

the goals present in the ,orking memory 'as in f#el tank

YES 'as in $ar+#retor

YES Engine ,ill t#rn over

WHY

)t has +een esta+lished that!. 3he engine is getting gas12. 3he engine ,ill t#rn over1 =,e need to kno,>

So that ,e $an make the $on$l#sion that3hen the pro+lem is the spark pl#gs.0

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Explanation and3ransparen$y in 'oal&Driven %easoning'as in f#el tankYes'as in $ar+#retor

YesEngine ,ill t#rn over

Wy

)t has +een esta+lished that!. 3he engine is getting gas1

2. 3he engine ,ill t#rn over13hen the pro+lem is the spark pl#gs.

How te engine is getting gas

3his follo,s from r#le

ifgas in f#el tank1 andgas in $ar+#retor

tenengine is getting gas.gas in f#el tank ,as given +y the #ser

gas in $ar+#retor ,as given +y the #ser

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"#tline





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Data&Driven %easoning

3he previo#s example exhi+its goal&driven sear$h. 3he sear$h ,as alsodepth&-rst sear$h

Breadth&-rst sear$h is more $ommon inData Driven reasoning

3he algorithm for this $ategory is simple

$ompare the $ontents of ,orkingmemory ,ith the $onditions of ea$h r#lein the r#le +ase a$$ording to the order ofthe r#les

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)f a pie$e of information that makes #pthe premise of a r#le is not the

$on$l#sion of some other r#le1 then that fa$t ,ill +e deemed

aska+le0

:or example the engine is getting gasis not aska+le in the premise of r#le !

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A #nreal Expert SystemExample%#le ! if

=not aska+le> the engine is getting gas1 andthe engine ,ill t#rn over1then

the pro+lem is spark pl#gs.%#le 2 if

the engine does not t#rn over1 and

the lights do not $ome onthenthe pro+lem is +attery or $a+les.

%#le if the engine does not t#rn over1 andthe lights do $ome on

then

the pro+lem is the starter motor.%#le if

there is gas in the f#el tank1 and

there is gas in the $ar+#retorthenthe engine is getting gas.

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3he premise1 the engine is getting gas is"3 aska+le1 so r#le ! fails and $ontin#e

to r#le 2

3he engine does not t#rn over is aska+le

S#ppose the ans,er to this /#ery isfalse1 so the engine ,ill t#rn over0 ispla$ed in ,orking memory

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3he prod#$tion system after eval#atingthe -rst premise of %#le 21 ,hi$h thenfails.

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3he prod#$tion system after eval#atingthe -rst premise of %#le 21 ,hi$h thenfails.

%#le 2 fails1 sin$e the -rst of t,oAD premises is false1 ,e move to

r#le

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3he data&driven prod#$tion system after$onsidering %#le 1 +eginning its se$ondpass thro#gh the r#les.

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3he data&driven prod#$tion system after$onsidering %#le 1 +eginning its se$ondpass thro#gh the r#les.

At this point1 all the r#les have+een $onsidered

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"#tline





d l d

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(odel&Based ExpertSystem

*#man expertise is an extremely $omplex$om+ination of 3heoreti$al kno,ledge

Experien$ed +ased pro+lem solving he#risti$s Example of past pro+lems and their sol#tions )nterpretive skills

3hro#gh years of experien$e1 h#man expert

develop very po,erf#l r#les for dealing ,ith$ommonly en$o#ntered sit#ations

3hese r#les are often highly $omplied0

d l d

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)n a r#le&+ased expert system example forsemi$ond#$tor fail#re analysis1 a des$riptiveapproa$h might +ase on

Dis$oloration of $omponents =+#rned&o#t> *istory of fa#lts in similar devi$es "+servation of $omponent +y ele$tron mi$ros$ope

*o,ever1 approa$hes that #se r#les to linko+servations and diagnosis do not o7er the+ene-ts of a deeper analysis of devi$e@sstr#$t#re and f#n$tion

( d l B d E

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A more ro+#st1 deeply explanatory approa$h,o#ld +egin ,ith a detailed model of thephysi$al str#$t#re of the $ir$#it and

e/#ations des$ri+ing the expe$ted +ehaviorof ea$h $omponent and their intera$tions.

A kno,ledge +ased reasoner ,hose analysis

is fo#nded dire$tly on the spe$i-$ation andf#n$tionality of a physi$al system is $alled a!"DE#-$%SED System

( d l B d E

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3he model +ased system tells its #ser,hat to expe$t1 and ,hen o+servationsdi7er from these expe$tations1 it ,ill lead

to identi-$ation of fa#lts

H#alitative model&+ased reasoningin$l#des A des$ription of ea$h $omponent in the devi$e A des$ription of the devi$es@ internal str#$t#re "+servation of the devi$es@ a$t#al performan$e

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(odel&Based Expert SystemExample

3he expe$ted o#tp#t val#e are given in => and the a$t#alo#tp#ts in I J

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At :1 ,e have a $on9i$t

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:inally1 ,e sho#ld note that in theexample1 there ,as ass#med to +e

a single fa#lty devi$e.3he ,orld is not al,ays this perfe$t (any other possi+le pro+lems may

o$$#r

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