Knowledge-Based Knowledge-Based SystemsSystems

Artificial IntelligenceArtificial Intelligence

Definition:Definition:

The activity of providing such machines as The activity of providing such machines as computers with the ability to display behavior computers with the ability to display behavior that would be regarded as intelligent if it were that would be regarded as intelligent if it were observed in humans.observed in humans.

HistoryHistory

1956, Dartmouth College. John McCarthy 1956, Dartmouth College. John McCarthy coined term. Same year, Logic Theorist (first coined term. Same year, Logic Theorist (first AI program. Herbert Simon played a part)AI program. Herbert Simon played a part)

Past 20 or so years, DOD and NSF have Past 20 or so years, DOD and NSF have funded AI research at top schools (Stanford, funded AI research at top schools (Stanford, Carnegie Mellon, etc.)Carnegie Mellon, etc.)

Desert Storm opened up new funding (smart Desert Storm opened up new funding (smart bombs, night vision)bombs, night vision)

Areas of Artificial IntelligenceAreas of Artificial Intelligence

ExpertExpertsystemssystems AIAI

hardwarehardware

RoboticsRobotics

PerceptivePerceptive systemssystems (vision,(vision, hearing)hearing)

NeuralNeuralnetworksnetworks

NaturalNatural languagelanguage

Learning

Artificial IntelligenceArtificial Intelligence

The Appeal of Expert The Appeal of Expert SystemsSystems

A computer program that attempts to code the A computer program that attempts to code the knowledge of human experts in the form of knowledge of human experts in the form of heuristics (i.e. a rule of thumb)heuristics (i.e. a rule of thumb)

Two distinctions from DSSTwo distinctions from DSS1. has the potential to extend the manager’s 1. has the potential to extend the manager’s

problem-solving ability beyond his or her normal problem-solving ability beyond his or her normal capabilitiescapabilities

2. the ability to explain how the solution was 2. the ability to explain how the solution was reachedreached

Know-ledgebase

User

Userinterface

Instructions &information

Solutions &explanations Knowledge

Inference engine

Problem Domain

Expert and knowledge engineer

Developmentengine ExpertExpert

systemsystemAn Expert An Expert

System ModelSystem Model

Expert system model - main Expert system model - main parts:parts:

User interfaceUser interface Knowledge baseKnowledge base Inference engineInference engine Development engineDevelopment engine

User InterfaceUser Interface

User enters:User enters:– InstructionsInstructions– InformationInformation

Expert system provides:Expert system provides:– SolutionsSolutions– Explanations ofExplanations of

» QuestionsQuestions

» Problem solutionsProblem solutions

}Menus, commands, natural language, GUI

Knowledge BaseKnowledge Base

Description of problem domainDescription of problem domain Rules: A knowledge representation Rules: A knowledge representation

techniquetechnique– such as ‘IF:THEN’ logicsuch as ‘IF:THEN’ logic– networks of rulesnetworks of rules

» Lowest levels provide evidenceLowest levels provide evidence

» Top levels produce 1 or more conclusionsTop levels produce 1 or more conclusions

» A conclusion is called a Goal variable.A conclusion is called a Goal variable.

Evidence

Conclusion

Conclusion

Evidence Evidence Evidence Evidence

Evidence Evidence Evidence

Conclusion

A Rule Set That Produces One Final

Conclusion

Cheetah Tiger Giraffe Zebra Ostrich Penguin Albatross

And And And And And And And And

Tawnycolor Dark

spots legs Long Black

strips Long neck

Can’t fly

Black&White

Swims FliesWell

Ungulate Bird

Mammal Carnivore

Or Or And And FeathersAnd

Or Or

Hair milk milk Hoofs

Flies Lays

eggsChews Gives

And

teeth Pointed Forward

Eyes

LEGEND:

Rules

ConditionsAction

(conclusions)Claws

Eats milk cud

R1 R2 R5 R6

R9 R10 R11 R12 R14R13 R15

R7 R8 R3 R4

A Rule Set That A Rule Set That Can Produce More Can Produce More Than One Final Than One Final ConclusionConclusion

Rule Selection Rule Selection

Selecting rules to efficiently solve a Selecting rules to efficiently solve a problem is difficultproblem is difficult

Some goals can be reached with only a few Some goals can be reached with only a few rules; rules 3 and 4 identify bird rules; rules 3 and 4 identify bird

Inference EngineInference Engine

Two basic approaches to using rulesTwo basic approaches to using rules

1. Forward reasoning (data driven)1. Forward reasoning (data driven)

2. Reverse reasoning (goal driven)2. Reverse reasoning (goal driven)

Forward ReasoningForward Reasoning(forward chaining)(forward chaining)

Rule is evaluated as: Rule is evaluated as: – (1) true, (2) false, (3) unknown(1) true, (2) false, (3) unknown

Rule evaluation is an iterative processRule evaluation is an iterative process When no more rules can fire, the reasoning When no more rules can fire, the reasoning

process stops even if a goal has not been process stops even if a goal has not been reachedreached

Rule 1Rule 1

Rule 3Rule 3

Rule 2Rule 2

Rule 4Rule 4

Rule 5Rule 5

Rule 6Rule 6

Rule 7Rule 7

Rule 8Rule 8

Rule 9Rule 9

Rule 10Rule 10

Rule 11Rule 11

Rule 12Rule 12

IF ATHEN B

IF CTHEN D

IF MTHEN E

IF KTHEN F

IF GTHEN H

IF ITHEN J

IF B OR DTHEN K

IF ETHEN L

IF K AND L THEN N

IF M THEN O

IF N OR OTHEN P

F

IF (F AND H)OR JTHEN M

IF (F AND H)OR JTHEN M

The The ForwardForward

ReasoningReasoningProcessProcess

T

TT

T

T

T

T

T

T

F

T

Legend:Legend: First pass

Second pass

Third pass

Reverse ReasoningReverse Reasoning(backward chaining) (backward chaining)

Divide problem into subproblemsDivide problem into subproblems

Try to solve one subproblemTry to solve one subproblem

Then try anotherThen try another

Rule 10

IF K AND LTHEN N

Rule 11

IF MTHEN O

Rule 12

IF N OR OTHEN P

Legend:

Problem

Subproblem

A Problem and Its SubproblemsA Problem and Its Subproblems

Rule 7

Rule 8

Rule 10

Subproblem

Legend:

Problem

IF B OR DTHEN K

IF ETHEN L

IF K AND LTHEN N

A Subproblem Becomes the New A Subproblem Becomes the New ProblemProblem

Rule 12

IF N OR O THEN P

T

Rule 1

Rule 2

Rule 3

Rule 9

Rule 11 Legend:Problems to be solved

Step 4

Step 3

Step 2

Step 1

Step 5

IF A THEN B

IF B OR DTHEN K

IF K AND LTHEN N

IF N OR O THEN P

IF CTHEN D

IF MTHEN E

IF ETHEN L

IF (F AND H)OR JTHEN M

IF MTHEN O

IF MTHEN O

T

The First Five The First Five Problems Are Problems Are

IdentifiedIdentifiedRule 7

Rule 10

Rule 12

If KThen F

Legend:Problems to be solved

If GThen H

If IThen J

If MThen O

Step 8

Step 9Step 7 Step 6

Rule 4

Rule 5

Rule 11Rule 6

T

IF (F And H)Or J

Then MT

Rule 9

T T

Rule 12

T

If N Or OThen P

The Next Four Problems AreThe Next Four Problems AreIdentifiedIdentified

Forward Versus Reverse Forward Versus Reverse ReasoningReasoning

Reverse reasoning is faster than forward Reverse reasoning is faster than forward reasoningreasoning

Reverse reasoning works best whenReverse reasoning works best when– there are multiple goal variablesthere are multiple goal variables– there are many rulesthere are many rules– all or most rules do not have to be examined in all or most rules do not have to be examined in

the process of reaching a solutionthe process of reaching a solution

Handling UncertaintyHandling Uncertainty

Two types of uncertaintyTwo types of uncertainty– RulesRules– ConditionsConditions

Certainty factors (CFs) range from 0.00 to Certainty factors (CFs) range from 0.00 to 1.001.00

Development EngineDevelopment Engine

Programming languages Lisp, Prolog, and Programming languages Lisp, Prolog, and recently C++recently C++

Expert system shellsExpert system shells

Role of the Systems Role of the Systems AnalystAnalyst

Knowledge engineers work with the expert Knowledge engineers work with the expert in designing expert systemsin designing expert systems

Beyond traditional analyst skills, the Beyond traditional analyst skills, the following skills are neededfollowing skills are needed– understand how the expert applies his or her understand how the expert applies his or her

knowledgeknowledge– be able to extract the description of the be able to extract the description of the

knowledge (rules as well as facts)knowledge (rules as well as facts)

System Development System Development ProcessProcess

Initiate the development processInitiate the development process Develop the expert system prototypeDevelop the expert system prototype User participationUser participation Expert system maintenanceExpert system maintenance

Prototyping ApproachPrototyping Approach

A new player: the expertA new player: the expert Delayed user involvementDelayed user involvement Need for maintenanceNeed for maintenance

Systems analystSystems analyst

Study the Problem domain

Study theStudy the problemproblemdomaindomain

Define the problemDefine the problem

Specify the rule setSpecify the rule set

step 1step 1

step 2step 2

step 3step 3

step 4step 4

step 5step 5

Test the prototype systemTest the prototype system

Construct the interfaceConstruct the interface

Maintain the systemMaintain the system

ExpertExpert UserUser

ConductConductuser testsuser tests

Use theUse thesystemsystem

step 6step 6

step 7 step 7

Prototyping Is Incorporated in the Development of an Prototyping Is Incorporated in the Development of an Expert SystemExpert System

step 8step 8

Nee

d t

o r

edes

ign

Nee

d t

o r

edes

ign

Nee

d t

o r

edes

ign

Nee

d t

o r

edes

ign

Example:Example:Financial Expert SystemFinancial Expert System

Credit approvalCredit approval Knowledge base for the example consists of Knowledge base for the example consists of

rules and a mathematical modelrules and a mathematical model User interfaceUser interface Five decision categories; credit amount Five decision categories; credit amount

influences weightingsinfluences weightings

Weightings of the Information CategoriesWeightings of the Information Categories

Financial strength 0.65 0.70Payment record 0.18 0.20Customer background 0.10 0.05Geographical location 0.05 0.03Business potential 0.02 0.02

Total 1.00 1.00

$5,000 to $20,000 toCategory $20,000 $50,000

Expert System Expert System AdvantagesAdvantages

To managersTo managers– Consider more alternativesConsider more alternatives– Apply high level of logicApply high level of logic– Have more time to evaluate decision rulesHave more time to evaluate decision rules– Consistent logicConsistent logic

To the firmTo the firm– Better performance from management teamBetter performance from management team– Retain firm’s knowledge resourceRetain firm’s knowledge resource

Expert System Expert System DisadvantagesDisadvantages

Can’t handle inconsistent knowledgeCan’t handle inconsistent knowledge

Can’t apply judgment or intuitionCan’t apply judgment or intuition

Neural NetworksNeural Networks

Expert systems should be able to learn, and Expert systems should be able to learn, and improve their performanceimprove their performance

Neural net design -- a bottom-up approach Neural net design -- a bottom-up approach to modeling human intuitionto modeling human intuition

The Human BrainThe Human Brain

Neuron -- the information processorNeuron -- the information processor– Input -- dendritesInput -- dendrites– Processing -- somaProcessing -- soma– Output -- axonOutput -- axon

Neurons are connected by the synapseNeurons are connected by the synapse

Soma(processor)

Axon

Synapse

Dendrites (input)

Axonal Paths (output)

Simple Biological NeuronsSimple Biological Neurons

Artificial Neural Systems Artificial Neural Systems (ANS)(ANS)

McCulloch-Pitts mathematical neuron McCulloch-Pitts mathematical neuron function (late 1930s)function (late 1930s)

Hebb’s learning law (early 1940s)Hebb’s learning law (early 1940s) NeurocomputersNeurocomputers

– Marvin Minsky’s Snark (early 1950s)Marvin Minsky’s Snark (early 1950s)– Rosenblatt’s Perceptron (mid 1950s)Rosenblatt’s Perceptron (mid 1950s)

Current MethodologyCurrent Methodology

Mathematical modelsMathematical models Complex networksComplex networks Repetitious training -- the ANS “learns” by Repetitious training -- the ANS “learns” by

example. An ANS can learn; an expert example. An ANS can learn; an expert system cannot.system cannot.

y1

y2

y3

yn-1

y

w1

w2

w3

wn-1

Single Artificial NeuronSingle Artificial Neuron

The Multi-The Multi-Layer Layer

PerceptronPerceptron

Yn2

ININnn

OUTOUTnnOUTOUT11

ININ11

YY11

Input Input LayerLayer

OutputLOutputLayerayer

Prerequisite Activities for Prerequisite Activities for the EISthe EIS

Information Information needsneeds

InformationInformationtechnology technology standardsstandards

InformationInformationsystems plansystems plan

CorporateCorporatedata modeldata model

Production andProduction andperformance systemsperformance systems

EIS

Analysis ofAnalysis oforganizationorganization