Knowledgebased Systems

8/9/2019 Knowledgebased Systems

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Knowledgebased systems

Dr.M.R.NarasingaRao

1Dr.M.R.NarasingaRao


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Knowledge based Systems A computer program which solves problems within a

limited and specifc feld using data on the problem!nowledge related to the problem and intelligentdecision ma!ing capabilities

"he nature o# the problem the solution to the

problem is not well defned or not !nown be#orehand

Applications o# !nowledge based systems

$rocess control industries

%n#erence ma!ing $rocess monitoring

&ault diagnosis and alarm management

$rocess scheduling and optimi'ation

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Structure o# a !nowledgebased system

%n#erence)ngine

* Reasoning

Mechanism+

Datebase* ,onte-t +

Knowledgebase

* Rules or$roductions+

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Knowledge based systemKnowledge base

Knowledge and e-pertise in the specifc domain* domain specifc #acts and heuristics +

Represented in various #orms * i#0then rules+

Database Regarded as short term memory

,urrent status o# the problem in#erence stateshistory o# the solutions to date

New in#ormation #rom e-ternal sources such assensors and human inter#aces is stored in thedatabase.

,onte-t o# the decision ma!ing process.

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Knowledge based system%n#erence )ngine Driver program o# the !nowledge based system

Depending on the data in the database thein#erence engine applies and operates on the!nowledge in the !nowledge source to solveproblems and arrive at conclusions.

%n#erence mechanisms

Data structure selected #or specifc #orm o#!nowledge representation determines the natureo# the program created as an in#erence engine.



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Knowledge base system

%nter#ace"o interact with the overall system

"o browse through the !nowledge source

"o edit the !nowledge source

)-3 !eyboards screen displays sensorstransducers outputs #rom computer programs e-pert systems etc4.

5ell developed !nowledge based systems

!nowledge ac6uisition #acility

e-planation #eature

%7M8$*D+K9

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Architectures o# K;S"hree commonly used architectures

$roduction systems &rame based systems

;lac! board systems

Some commonalities and some distinguishing #eatures

$roduction systems Rule based systems

Appropriate and representation and processing o#!nowledge in problem solutions in A%

An e-pert system


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$roduction Systems =peration o# a rule based system

New data are generated #rom e-ternal world andstored in appropriate locations in the databasesystem . "his is called a new conte-t

"he in#erence engine tries to match the new data

with the condition part o# the rule in the!nowledge base. Rule searching

%# the condition part o# the rule matches with thedata the rule is fred

&iring o# a rule generates the new #acts and this inturn may #orm a new conte-t

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$roduction Systems "wo strategies

&orward ,haining

;ac!ward ,haining

&orward ,haining

data driven search method "he rule base is searched to match an i# part o# a

rule with the data?conte-t

Direct strategy

;ottom0up approach Actions3 deletion creation and updation o# data

&orward production system

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$roduction Systems ;ac!ward ,haining

A hypothesi'ed conclusion is matched with therules in the !nowledge base in order to determinethe conte-t that supports the particularconclusion.

%# enough #acts that supports a hypothesis thehypothesis is accepted

Diagnosis and theorem proving

ogical e-planation has to be attached to each

action ;ac!ward chaining system



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$roduction Systems ,onBict resolution methods

,onBict Set * ,onte-t data condition part o# the rule+

Methods o# conBict resolution

&irst Match

"oughest Match

$rivileged Match Most recent Match

&irst Match

"he very frst rule that is satisfed during searching will befred

Simple strategy per#ormance

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$roduction systems"oughest Match

the rule with the most conditionelements will be fred

$rivileged Match

the rule with the highest priority will beselected

$riority may be assigned based on the

toughness o# the match Signifcance and conse6uences o# its

acts



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$roduction SystemsMost recent match

"he condition part satisfes the mostrecent entries o# data

Cigher priority is given to morerecently arrived data in the database

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&rame based Systems "he #rame that contains action rules are called

action #rames "he #rames that defne obect distribution are

called situational #rames

A #rame may consist o# #rame label and a set o#

slots )ach slot may contain a set o# statements or

another #rame that is at the ne-t lower level inthe hierarchy than the present level

Knowledge is represented in a hierarchical way.



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;lac!board Systems ,ooperative problem solving architecture

Elobal database called blac!board

Several intelligent modules called !nowledge sources

,ontrol unit manages the operation o# an entiresystem

;lac!board is shared by and visible to the entiresystem

Cas the Be-ibility o# accommodating diFerent types o#!nowledge sources

DiFerent methods o# !nowledge representation and

processing Knowledge sources are not arranged in a hierarchical

manner and will cooperate as e6ual partners inma!ing a !nowledge based decision.

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;lac!board Systems Knowledge sources interact with shared data region

under the supervision o# control unit 5hen the data in the blac!board change which

corresponds to a change in the conte-t the !nowledgesource would be triggered in an opportunistic mannerand an appropriate decision would be made

"his decision could lead to #urther changes to theblac!board data and subse6uent triggering o# other!nowledge source

Data may be changed by e-ternal means as well as by

!nowledge source actions )-ternal data entering the system go directly to the

blac!board . G% is lin!ed to the blac!board.

"he operation o# the blac!board based system iscontrolled by its control unit.

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(@Dr.M.R.NarasingaRao

=b t = i t d


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=bect =riented$rogramming %n a conventional program written in a procedural

language

%nstructions and data structures are integrated togetherthroughout the program

)ven a small change to a data structure could ma!e theprogram non0#unctional clearly indicating an advantage o#

the obect oriented approach. A !nowledge based system shell is ust an empty

!nowledge based system without any domain !nowledge.

%t provides an in#erence engine and a !nowledgerepresentation structure that can be used as a

programming tool #or K;S in diFerent application areas. "he !nowledge source can be built incrementally and is

reletively easy to e-pand

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)-pert systems)-pert System

So#tware system with high symbolic anddescriptive in#ormation content whichcan simulate the per#ormance o# a

human e-pert in a specifc feld?domain. %t is a special type o# a !nowledge

based system

Knowledgebase database and anin#erence engine and a human?machineinter#ace

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)-pert System

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" ! # ! l d


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"as!s o# !nowledgeengineering

Knowledge )ngineer

Eathers the e-pertise about a particular domain #romone?more e-perts and organi'es that !nowledge into a#orm re6uired by the particular e-pert system toolthat is to be used.

Ac6uisition o# !nowledge that is pertinent #romdiFerent sources * e-perts literature media 4+

%nterpretation and integration o# the !nowledge* #romvarious sources and in diFerent #orms+

Representation o# the !nowledge within the

!nowledgebased system * suitable structurelanguage incomplete !nowledge presence o#analytical models availability o# past e-perienceetc4+

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Applications )ducation and training

Medical diagnosis and prescription o# treatment Mineral e-ploration

%nterpretation o# satellite imagery

&inancial advising

egal consultations

"a- return preparation

System trouble shooting and maintenance

$lanning and scheduling

5eather #orecasting

"rouble shooting

System control

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Knowledge representation and processing

An appropriate representation o# !nowledge

including intuition and heuristic !nowledge iscentral to the development o# machineintelligence and o# !nowledge based systems

"wo types o# !nowledge are needed #or a K;S

Knowledge o# the problem Knowledge regarding methods #or solving the

problem

5ays o# representing !nowledge

ogic semantic networ!s #ramesproduction systems and #u''y logic



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Semantic networ!s Gse#ul #or graphical representation and

processing o# !nowledge Knowledge obects are represented in a networ!

Relationships are represented by arcs

Arcs are directed

Knowledge represented by semantic networ! isprocessed using networ! searching proceduresusually starting with some available data andending with a set o# conclusions

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Semantic networ!s

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,risp sets

%t is a collection o# elements within acrisp boundary. Since there cannotbe any elements on the boundarythis is called a crisp set.

Jenn diagram universal set null set-LA - doesnt belong to A

=perations on sets

,omplement union intersectionsubset proper subset

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ogic Disunction3 "he disunction o# two

propositions is A J ;

"his operation in logic corresponds tothe union operation o# sets

,onunction

"he conunction o# two propositions Aand ; is A ;

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ogic %mplication3 i# A and ; are two

propositions then A implies ; means %&A "C)N ;. this may be denoted by A;

"he two propositionsA and B are

e6uivalent i#A B and also B A. Thismay be denoted by either A B orA B.

Note that the statement A B is trueeither if both A and B are trueO or Pi#bothA and B are false.

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aws o# logic

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Rules o# %n#erence

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$ropositional calculus and


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$ropositional calculus is the branch o# logic wherepropositions are used in logic calculations

,alculus3 approach o# calculation

Repositional calculus

$redicate calculus $redicate statement3 $redicate*Argument+

isCigh* - +


$ropositional calculus andpredicate calculus


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So#t computing

%ntelligent and !nowledgebased systems Cumans can eFectively handle incomplete

imprecise #u''y in#ormation in ma!ing intelligentdecisions

&u''ylogic probability theory neural networ!sand genetic algorithms are cooperatively used

!nowledge representation and #or mimic!ing thereasoning and decision ma!ing process o# ahuman

Appro-imate reasoning

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&u''y ogic

Dr.M.R.NarasingaRao /:

Gse#ul in representing human !nowledge in a specifc

domain o# application and Reasoning that !nowledge toma!e use#ul in#erences

;inary logic is crisp and allows #or only two states

;inary logic cannot handle #u''y descriptors

Realistic e-tension o# binary crisp logic to 6ualitative

subective and appro-imate situations which o#tene-ist in problems o# intelligent machines

"he !nowledgebase is represented by i#0then rules o##u''y descriptors

)- o# #u''y rule3 i# the speed is low and the target is#ar then moderately increase the power.

A #u''y descriptor is described by a membership#unction


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&u''y ogic "he membership #unction gives a membership grade

between @ and 1 #or each possible value o# #u''y descriptor

A #u''y set A is represented by a M&

&'8-L A97QA*-+38@19

"his value gives grade o# membership o# - in A

@QA*-+1 implies P the membership is not crispO * i.e.

#u''y+ "he element - has some possibility o# within the &u''y set

A.

"he element - has some complementary possibility o#outside o# A. i.e. the element is on the boundary o# the set

A. A #u''y rule may be represented as grouping o#

membership #unction

%# A1 and ;1 then ,1T i# A( and ;( then ,(.



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Neural Networ!s

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ANN are massively connected networ!s and

represent parallel distributed processingstructures

%nspiration #or NN

Appro-imating nonlinear #unctions

earning Neural Networ!s

neurons layers and synapses andActivation #unction

=utput o# the neural networ!s


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=peration o# a Neural Networ!


i l #


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Main classes o# NN

#eed #orward Neural Networ!

#eedbac! Networ!

&eed #orward Neural Networ!

M$

signal Bow

no #eedbac! paths

learning is achieved through e-amples

supervised learning

learning algorithm

&eedbac! Networ!s "he output o# one or more nodes * output layer+

are #ed bac! to one?more nodes in a previous layer *hidden layer?input layer+ or even to the same node.



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$rovides the capability o# memory

Copfeld Networ!s

"he input layer and a Copfeld layer

each node in the input layer is connected to only onenode in the output layer

"he outputs o# the networ! are #ed bac! to the input nodesvia time delay * providing memory+ and synaptic weights

Nodes in the Copfeld layer use nonlinear activation#unctions.

Some classes o# NN use unsupervised learning algorithm

Synaptic weights are adusted based on the input values tothe networ! not by comparing the networ! output with the

desired output. Gnsupervised learning is also called sel# organi'ed learning

Gse#ul #or pattern classifcation and grouping o# data

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E ti l ith


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Eenetic algorithms Eenetic algorithms belong to the area o# evolutionary

computing

)volutionary computing

=ptimi'ation approach

Search is made to evolve a solution algorithm

Retains the most ft components in a procedure

Analogous to biological evolution through selectioncrossover and mutation

$lays an optimal role in the development o# optimal andsel# improving intelligent machine

,haracteristics %t is based on multiple searching point ?solution

candidates

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An entirely new population o# possible solutions isproduced in this manner by mating the bestindividuals *i.e. individuals with best solutions+ #rom

the current generation. "he new generation willcontain a higher proportion o# the characteristicspossessed by the PftO members o# the previousgeneration.

;y #avoring the mating o# the individuals who are

more ft *i.e. who can provide better solutions+ themost promising areas o# the search space would bee-ploited. A EA determines the ne-t set o# searchingpoints using the ftness values o# the current

searching points which are widely distributedthroughout the searching space

%t uses the mutation operation to escape #rom a localminimum.

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$robabilistic Reasoning 5ithin the area o# so#t computing

May be viewed analogous to #u''y logic reasoning Gncertainty in place o# #u''iness

$robability distribution? density #unctionmembership #unctions

;ayesian Approach Suppose that an observation d is made, and it may

belong to one of several classes ci. The Bayesrelation states where

$*,i? d+ 7 $*d?,

i+U$*,

i+ ? $*d+

$*,i? d+7 given that the observation is d the

probability that it may belong to class ,i

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Dr.M.R.NarasingaRao 2@

$robablistic Reasoning


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$robablistic Reasoning Application areas

&orecasting and signal analysis signal analysis andfltering parameter estimation system identifcation

Summari'ing the biological analogies o# so#t ,omputingtechni6ues

&u''y techni6ues3 Appro-imates human !nowledge and

reasoningNeural Networ!s3 simplifed representation o# neuron

structure o# a brain

Eenetic algorithms3 #ollows the process o# evolution inbiologial species

$robablistic techni6ues3 Analy'es the random #uture actiono# a human.



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