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

Acknowledgements t Dr. Russell Ching ( MIS Dept ) Source Materiel / Graphics t Edie Schmidt ( UMS ) - Graphic Design t Prentice Hall Publishing (Permissions) u Martin, Analysis and Design of Business Information Systems, 1995

Agenda t Gate Assignment Problem t Artificial Intelligence t Expert Systems ( ES) t ES Examples

In the Airline Industry t United Airlines' GADS (Gate Assignment Display System) t Trans World Airlines' GATES (Gate Assignment and Tracking Expert System)

Boeing 747, capacity Lockheed L-1011, 252 capacity

Boeing 767, capacity Boeing 727, capacity

McDonnell Douglas DC-9, MD capacity

Gate Assignment Problem

Constraints: t Matching size of aircraft to gate 8 different types with United 6 with TWA t Minimizing distances between connecting flights t Foreign vs. domestic flight

GATES Constraints t Constraints without exceptions u Gate size t Constraints with exceptions u International versus domestic flights t Constraints with changing tolerances u Turn-around times

t Guidelines u Taxiway congestion t Convenience constraints u Time between flights u Distance between connecting flights GATES Constraints GATES Constraints

ES benefits: t Task of scheduling gate assignments for a month reduced from 15 hours to 30 seconds. t ES can be transferred to other airport operations, reducing training / operating costs. Gate Assignment

Benefits (Cont.) t Decrease susceptibility of schedule to moods and whims of schedulers. t Gate assignments can be done on demand with little interference to current operations. Gate Assignment

Benefits (Cont.) t Managers can review impact of changes, implement changes (i.e., what-if analysis). t ES integrated into airlines' major operations / scheduling systems through direct electronic interfaces, thus expediting scheduling. Gate Assignment

Artificial Intelligence ( AI) Effort to develop computer-based systems that behave like humans: that behave like humans: u learn languages u accomplish physical tasks u use a perceptual apparatus u emulate human thinking

AI Branches t Natural Language t Robotics t Perceptive Systems t Expert Systems t Intelligent Machines

Human Processing Capabilities t Induction: u act on inconsistently formatted data u fill in the gaps u CN U RD THS u Wheel of Fortune t Adaptiveness

Human Processing Capabilities t Insight: u creativity u create alternatives u chess game u perspicuous grouping

Perspicuous Grouping t Recognize that we can handle only a few alternatives u Short Term Memory ( STM ) u Miller’s 7 +/- 2 Rule t Zero in on a few viable alternatives t Enumerate / select best t Satisficing, rather than optimizing t Herbert Simon’s 1958 Chess prediction

Computer Processing Capabilities t Handle large volume of data u quickly t Detect signals where humans sense “ noise ” t Tireless

Computer Capabilities t Consistent t Objective u no “ selective perception ” t Not distracted t Minimal “ down-time”

Issue A Stanford Research Institute (SRI) scientist once said, “You needn’t fear intelligent machines. Maybe they’ll keep us as pets.” u Will intelligent machines replace us? u Why or why not? WHAT DO YOU THINK?

What is an ES? t Feigenbaum, 1983 “intelligent computer program using knowledge / inference procedures to solve problems difficult enough to require significant human expertise ; a model of the expertise of the best practitioners”

Components of an Expert System Knowledge Base Recom- mended Action Inference Engine User Interface Explanation Facility Facts and Rules User Knowledge Acquisition Facility

Rule Induction Case Classified Through Deduction Rules Induced From Example Cases Individual Cases Applied to the Rules Induction (Inductive Logic) Deduction (Deductive Logic)

Pay or Reject Type of Account Credit Rating Overdraft for Single or Multiple Checks PayPayRegularRegularGoodGoodMultipleMultiple PayPayStudentStudentUnknownUnknownSingleSingle RejectRejectStudentStudentPoorPoorSingleSingle RejectRejectStudentStudentGoodGoodMultipleMultiple PayPayStudentStudentGoodGoodSingleSingle DecisionDecision Decision Attributes Check Overdraft Cases

Pay or Reject Type of Account Credit Rating Overdraft for Single or Multiple Checks DecisionDecision Decision Attributes PayPayRegularRegularUnknownUnknownMultipleMultiple PayPayRegularRegularGoodGoodSingleSingle RejectRejectRegularRegularPoorPoorSingleSingle RejectRejectStudentStudentUnknownUnknownMultipleMultiple RejectRejectRegularRegularUnknownUnknownMultipleMultiple Check Overdraft Cases (Cont.)

Pay or Reject Type of Account Credit Rating Overdraft for Single or Multiple Checks ??RegularRegularUnknownUnknownSingleSingle Pay or Reject?

Bank Overdraft Application t 340 Cases of check overdrafts t Classification Variable: u Check unpaid(0) or paid (1)

ID3 DECISION TREE PayRejectPay RejectPay Reject Pay RejectPay RejectPayReject 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 YesNo YesNoYesNo ACT*DIFF <19.6 Overall Classification Rate: 97.7%

Reasons For Using ES t Consistent t Never gets bored / overwhelmed t Replace absent, scarce experts t Quick response time

ES Reasons t Reduced down-time t Cheaper than experts t Integration of multi-expert opinions t Eliminate routine / unsatisfactory jobs for people

ES Limitations t High development cost t Limited to relatively simple problems u operational mgmt level t Can be difficult to use t Can be difficult to maintain

When to Use ES t High potential payoff OR OR t Reduced risk t Need to replace experts u Campbell’s Soup

When to Use ES t Need more consistency than humans t Expertise needed at various locations at same time t Hostile environment dangerous to human health

ES Versus DSS t Problem Structure: u ES: structured problems v clear v consistent v unambiguous u DSS: semi-structured problems

ES Versus DSS t Quantification: u DSS: quantitative u ES: non-mathematical reasoning IF A BUT NOT B, THEN Z t Purpose: u DSS: aid manager u ES: replace manager

Issue Does your company use Expert Systems (ES)? u How do they? u How might they? WHAT ARE YOUR EXPERIENCES?

MYACIN t Diagnose patient symptoms (triage) u free doctors for high-level tasks t Panel of doctors u diagnose sets of symptoms u determine causes u 62% accuracy

MYACIN t Built ES with rules based on panel consensus t 68% accuracy u Why better than doctors? t Heuristics

Stock Market ES t Reported by Chandler, 1988 t Expert in stock market analysis u 15 years experience u published newsletter t Asked him to identify data used to make recommendations

Stock Market ES t 50 data elements identified t Reduced to 30 u redundancy u not really used u undependable t Predicted for 6 months of data whether stock value would increase, decrease, or stay the same

Stock Market ES t Rule-based ES built t Discovered that only 15 data elements came into play t Refined the ES model t Results were better than expert WHY? WHY?

USA Expert Systems Manufacturing Planning: HICLASS - Hughes (process plans, manufacturing instructions) CUTTECH - METCUT (plans for machining operations) XPSE-E - CAM-I (plans for part fabrication)

USA Expert Systems Manufacturing Control: IMACS - DEC (plans for computer hardware fabrication and assembly) IFES - Hughes (models dynamic flow of factory information)

USA Expert Systems Factory Automation: Move - Industrial Technology Institute (material handling) Dispatcher - Carnegie Group, Inc. (materials handling system) GMR - GM Corp. (flexible automation assembly system) FMS/CML - Westinghouse (simulation for FMS design, planning, control)

Issue “Expert systems are dangerous. People are likely to be dependent on them rather than think for themselves.” WHAT DO YOU THINK?

Points to Remember t What is AI? t What is an ES? t When to use an ES t Differences between ES and DSS t ES examples