Artificial Intelligence (AI) Dr. Merle P. Martin MIS Department CSU Sacramento.

Artificial Intelligence (AI)Artificial Intelligence (AI)

Dr. Merle P. MartinDr. Merle P. MartinMIS DepartmentMIS DepartmentCSU SacramentoCSU Sacramento

AcknowledgementsAcknowledgements Dr. Russell ChingDr. Russell Ching ( (MIS DeptMIS Dept) )

Source Materiel / GraphicsSource Materiel / Graphics Edie SchmidtEdie Schmidt ( (UMSUMS) - Graphic Design) - Graphic Design Prentice Hall PublishingPrentice Hall Publishing (Permissions) (Permissions)

Martin, Martin, Analysis and Design ofAnalysis and Design of Business Information SystemsBusiness Information Systems, 1995, 1995

AgendaAgenda Gate Assignment ProblemGate Assignment Problem Artificial IntelligenceArtificial Intelligence Expert Systems (Expert Systems (ES)ES) ES ExamplesES Examples

In the Airline IndustryIn the Airline Industry United Airlines' GADS United Airlines' GADS

(Gate Assignment Display (Gate Assignment Display System)System)

Trans World Airlines' GATES Trans World Airlines' GATES (Gate Assignment and Tracking (Gate Assignment and Tracking Expert System)Expert System)

Boeing 747, Boeing 747, 387-427 capacity387-427 capacity

Lockheed L-1011, Lockheed L-1011, 252 capacity252 capacity

McDonnell Douglas DC-9, MD-80McDonnell Douglas DC-9, MD-80 73-132 capacity73-132 capacity

Gate Assignment ProblemGate Assignment Problem

Gate Assignment ProblemGate Assignment ProblemConstraints:Constraints: Matching size of aircraft to gate Matching size of aircraft to gate

8 different types with United 8 different types with United 6 with TWA 6 with TWA

Minimizing distances between Minimizing distances between connecting flightsconnecting flights

Foreign vs. domestic flightForeign vs. domestic flight

GATES ConstraintsGATES Constraints Constraints without exceptionsConstraints without exceptions

Gate sizeGate size Constraints with exceptionsConstraints with exceptions

International versus domestic International versus domestic flightsflights

Constraints with changing tolerancesConstraints with changing tolerances Turn-around timesTurn-around times

GuidelinesGuidelines Taxiway congestionTaxiway congestion

Convenience constraintsConvenience constraints Time between flightsTime between flights Distance between Distance between

connecting flightsconnecting flights

GATES Constraints GATES Constraints

ES benefits: ES benefits: Task of scheduling gate Task of scheduling gate

assignments for a month assignments for a month reduced from 15 hours reduced from 15 hours to 30 seconds.to 30 seconds.

ES can be transferred to other ES can be transferred to other airport operations, reducing airport operations, reducing training / operating costs.training / operating costs.

Gate Assignment Gate Assignment

Benefits (Cont.) Benefits (Cont.) Decrease susceptibility of Decrease susceptibility of

schedule to moods and schedule to moods and whims of schedulers. whims of schedulers.

Gate assignments can be done Gate assignments can be done on demand with little interference on demand with little interference to current operations. to current operations.


Benefits (Cont.)Benefits (Cont.) Managers can review impact Managers can review impact

of changes, implement changes of changes, implement changes (i.e., what-if analysis).(i.e., what-if analysis).

ES integrated into airlines' ES integrated into airlines' major operations / scheduling major operations / scheduling systems through direct electronic systems through direct electronic interfaces, thus expediting interfaces, thus expediting scheduling.scheduling.


Artificial Intelligence (Artificial Intelligence (AI)AI)Effort to develop Effort to develop

computer-based systemscomputer-based systems that behave like humans:that behave like humans:

learn languageslearn languages accomplish physical tasksaccomplish physical tasks use a perceptual apparatususe a perceptual apparatus emulate human thinkingemulate human thinking

AI BranchesAI Branches Natural LanguageNatural Language RoboticsRobotics Perceptive SystemsPerceptive Systems Expert SystemsExpert Systems Intelligent MachinesIntelligent Machines

Human Processing Human Processing CapabilitiesCapabilities Induction:Induction:

act on inconsistently act on inconsistently formatted dataformatted data

fill in the gapsfill in the gaps CN U RD THSCN U RD THS Wheel of FortuneWheel of Fortune

AdaptivenessAdaptiveness

Human Processing Human Processing CapabilitiesCapabilities Insight:Insight:

creativitycreativity create alternativescreate alternatives chess gamechess game perspicuous groupingperspicuous grouping

Perspicuous GroupingPerspicuous Grouping Recognize that we can Recognize that we can

handle only a few alternativeshandle only a few alternatives Short Term Memory (Short Term Memory (STMSTM)) Miller’s 7 +/- 2 RuleMiller’s 7 +/- 2 Rule

Zero in on a few viable alternativesZero in on a few viable alternatives Enumerate / select bestEnumerate / select best Satisficing, rather than optimizingSatisficing, rather than optimizing Herbert Simon’s 1958 Chess predictionHerbert Simon’s 1958 Chess prediction

Computer Processing Computer Processing CapabilitiesCapabilities Handle large volume of dataHandle large volume of data

quicklyquickly Detect signals Detect signals

where humans sense “where humans sense “noisenoise”” TirelessTireless

Computer CapabilitiesComputer Capabilities ConsistentConsistent ObjectiveObjective

no “no “selective perceptionselective perception”” Not distractedNot distracted Minimal “Minimal “down-time”down-time”

IssueIssueA Stanford Research Institute A Stanford Research Institute

(SRI) scientist once said, (SRI) scientist once said, “You needn’t fear intelligent “You needn’t fear intelligent machines. Maybe they’ll machines. Maybe they’ll keep us as pets.”keep us as pets.” Will intelligent machines Will intelligent machines

replace us?replace us? Why or why not?Why or why not?

WHAT DO YOU THINK?WHAT DO YOU THINK?

What is an ES?What is an ES? Feigenbaum, 1983Feigenbaum, 1983““intelligent intelligent computer programcomputer programusing knowledge / using knowledge / inference proceduresinference proceduresto solve problems difficult enoughto solve problems difficult enoughto require significant to require significant human expertisehuman expertise;;a a modelmodel of the expertise ofof the expertise ofthe best the best practitioners” practitioners”

Components of an Expert SystemComponents of an Expert SystemKnowledge Knowledge

BaseBase

Recom-Recom-mendedmendedActionAction

Inference Inference EngineEngine

User User InterfaceInterface

ExplanationExplanationFacilityFacility

Facts and RulesFacts and Rules

UserUser

KnowledgeKnowledgeAcquisitionAcquisition

FacilityFacility

Rule InductionRule Induction

Case Classified Through

Deduction

Rules Induced

From Example

Cases

Individual Cases

Applied to the Rules

InductionInduction(Inductive Logic)(Inductive Logic)

DeductionDeduction(Deductive Logic)(Deductive Logic)

Pay or Pay or Reject Reject Pay or Pay or Reject Reject

Type of Type of AccountAccountType of Type of AccountAccount

Credit Credit RatingRatingCredit Credit RatingRating

Overdraft Overdraft for Single for Single or Multiple or Multiple Checks Checks


PayPayPayPay RegularRegularRegularRegular GoodGoodGoodGood MultipleMultipleMultipleMultiplePayPayPayPay StudentStudentStudentStudent UnknownUnknownUnknownUnknown SingleSingleSingleSingle

RejectRejectRejectReject StudentStudentStudentStudent PoorPoorPoorPoor SingleSingleSingleSingleRejectRejectRejectReject StudentStudentStudentStudent GoodGoodGoodGood MultipleMultipleMultipleMultiple

PayPayPayPay StudentStudentStudentStudent GoodGoodGoodGood SingleSingleSingleSingle

DecisionDecisionDecisionDecision Decision AttributesDecision AttributesDecision AttributesDecision Attributes

Check Overdraft CasesCheck Overdraft Cases






DecisionDecisionDecisionDecision Decision AttributesDecision AttributesDecision AttributesDecision Attributes

PayPayPayPay RegularRegularRegularRegular UnknownUnknownUnknownUnknown MultipleMultipleMultipleMultiplePayPayPayPay RegularRegularRegularRegular GoodGoodGoodGood SingleSingleSingleSingle

RejectRejectRejectReject RegularRegularRegularRegular PoorPoorPoorPoor SingleSingleSingleSingleRejectRejectRejectReject StudentStudentStudentStudent UnknownUnknownUnknownUnknown MultipleMultipleMultipleMultipleRejectRejectRejectReject RegularRegularRegularRegular UnknownUnknownUnknownUnknown MultipleMultipleMultipleMultiple

Check Overdraft Cases Check Overdraft Cases (Cont.)(Cont.)




Overdraft Overdraft for Single for Single or Multiple or Multiple

Checks Checks

Overdraft Overdraft for Single for Single or Multiple or Multiple

Checks Checks

???? RegularRegularRegularRegular UnknownUnknownUnknownUnknown SingleSingleSingleSingle

Pay or Reject?Pay or Reject?

Bank Overdraft Bank Overdraft ApplicationApplication 340 Cases of check 340 Cases of check

overdraftsoverdrafts Classification Variable:Classification Variable:

Check unpaid(0) or paid (1)Check unpaid(0) or paid (1)

ID3 DECISION TREEID3 DECISION TREE176130

1165

60125

5957

501

21

01

20

480

956

556

40

354

22

21

01

168

053

115

10

015

154

142

12

1011

690

321

320

01

Pay Reject Pay

Reject Pay

Reject Reject Reject Pay Reject Pay

Pay Reject Pay Reject

DIFF<20.5

DIFF<10.5

DIFF<9.4DIFF<40.3

DIFF<42.2

CR *DIFF<6.5

CR *DIFF<.035

DIFF<1.65

ACT*DIFF<.175

CR*DIFF<5.5

COV*DIFF<1.5

DIFF<5.55

ACT*DIFF<3

Yes No

Yes No Yes No

ACT*DIFF<19.6

Overall Classification Overall Classification Rate: 97.7%Rate: 97.7%

Reasons For Using ESReasons For Using ES ConsistentConsistent Never gets bored / overwhelmedNever gets bored / overwhelmed Replace absent, scarce expertsReplace absent, scarce experts Quick response timeQuick response time

ES ReasonsES Reasons Reduced down-timeReduced down-time Cheaper than expertsCheaper than experts Integration of multi-expert opinionsIntegration of multi-expert opinions Eliminate routine / unsatisfactory Eliminate routine / unsatisfactory

jobs for peoplejobs for people

ES LimitationsES Limitations High development costHigh development cost Limited to relatively simple Limited to relatively simple

problemsproblems operational mgmt leveloperational mgmt level

Can be difficult to useCan be difficult to use Can be difficult to maintainCan be difficult to maintain

When to Use ESWhen to Use ES High potential payoffHigh potential payoff OROR Reduced riskReduced risk Need to replace expertsNeed to replace experts

Campbell’s SoupCampbell’s Soup

When to Use ESWhen to Use ES Need more consistency Need more consistency

than humansthan humans Expertise needed Expertise needed

at various locations at various locations at same timeat same time

Hostile environment Hostile environment dangerous to human healthdangerous to human health

ES Versus DSSES Versus DSS Problem Structure:Problem Structure:

ES: structured problemsES: structured problems clearclear consistentconsistent unambiguousunambiguous

DSS: semi-structured problemsDSS: semi-structured problems

ES Versus DSSES Versus DSS Quantification:Quantification:

DSS: quantitativeDSS: quantitative ES: non-mathematical ES: non-mathematical

reasoningreasoningIF A BUT NOT B, THEN ZIF A BUT NOT B, THEN Z

Purpose:Purpose: DSS: aid managerDSS: aid manager ES: replace managerES: replace manager

IssueIssue

Does your company use Does your company use Expert Systems (ES)?Expert Systems (ES)? How do they?How do they? How might they?How might they?

WHAT ARE YOUR WHAT ARE YOUR EXPERIENCES?EXPERIENCES?

MYACIN MYACIN Diagnose patient Diagnose patient

symptoms (triage)symptoms (triage) free doctors for free doctors for

high-level taskshigh-level tasks Panel of doctorsPanel of doctors

diagnose sets of symptomsdiagnose sets of symptoms determine causesdetermine causes 62% accuracy62% accuracy

MYACINMYACIN Built ES with rules Built ES with rules

based on panel consensusbased on panel consensus 68% accuracy68% accuracy

Why better than doctors?Why better than doctors? HeuristicsHeuristics

Stock Market ESStock Market ES Reported by Chandler, 1988Reported by Chandler, 1988 Expert in stock market analysisExpert in stock market analysis

15 years experience15 years experience published newsletterpublished newsletter

Asked him to identify data Asked him to identify data used to make recommendationsused to make recommendations

Stock Market ESStock Market ES 50 data elements identified50 data elements identified Reduced to 30Reduced to 30

redundancyredundancy not really usednot really used undependableundependable

Predicted for 6 months of data Predicted for 6 months of data whether stock value would increase, whether stock value would increase, decrease, or stay the samedecrease, or stay the same

Stock Market ESStock Market ES Rule-based ES builtRule-based ES built Discovered that only Discovered that only

15 data elements came into play 15 data elements came into play Refined the ES modelRefined the ES model Results were better than expertResults were better than expert WHY?WHY?

USA Expert Systems USA Expert Systems

Manufacturing Planning:Manufacturing Planning:

HICLASS - Hughes HICLASS - Hughes (process plans, manufacturing instructions)(process plans, manufacturing instructions)

CUTTECH - METCUT CUTTECH - METCUT (plans for machining operations)(plans for machining operations)

XPSE-E - CAM-I XPSE-E - CAM-I (plans for part fabrication)(plans for part fabrication)

USA Expert Systems USA Expert Systems

Manufacturing Control:Manufacturing Control:

IMACS - DEC IMACS - DEC (plans for computer hardware fabrication (plans for computer hardware fabrication and assembly) and assembly)

IFES - Hughes IFES - Hughes (models dynamic flow of factory information)(models dynamic flow of factory information)

USA Expert Systems USA Expert Systems Factory Automation:Factory Automation:Move - Industrial Technology Move - Industrial Technology Institute Institute (material handling)(material handling)

Dispatcher - Carnegie Group, Inc. Dispatcher - Carnegie Group, Inc. (materials handling system)(materials handling system)

GMR - GM Corp. GMR - GM Corp. (flexible automation assembly system)(flexible automation assembly system)

FMS/CML - Westinghouse FMS/CML - Westinghouse (simulation for FMS design, planning, control)(simulation for FMS design, planning, control)

IssueIssue““Expert systems are Expert systems are

dangerous. People are dangerous. People are likely to be dependent on likely to be dependent on them rather than think them rather than think for themselves.”for themselves.”

WHAT DO YOU THINK?WHAT DO YOU THINK?

Points to RememberPoints to Remember What is AI?What is AI? What is an ES?What is an ES? When to use an ESWhen to use an ES Differences between Differences between

ES and DSSES and DSS ES examplesES examples

Artificial Intelligence (AI) Dr. Merle P. Martin MIS Department CSU Sacramento.

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