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Artificial Intelligence (AI)

Artificial Intelligence (AI). Dr. Merle P. Martin MIS Department CSU Sacramento. Acknowledgements. Dr. Russell Ching ( MIS Dept ) Source Materiel / Graphics Edie Schmidt ( UMS ) - Graphic Design Prentice Hall Publishing (Permissions)

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Artificial Intelligence (AI)

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

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

  3. Agenda • Gate Assignment Problem • Artificial Intelligence • Expert Systems (ES) • ES Examples

  4. In the Airline Industry • United Airlines' GADS (Gate Assignment Display System) • Trans World Airlines' GATES (Gate Assignment and Tracking Expert System)

  5. Boeing 747, 387-427 capacity Lockheed L-1011, 252 capacity

  6. Boeing 767, 170-227 capacity Boeing 727, 115-134 capacity

  7. McDonnell Douglas DC-9, MD-8073-132 capacity

  8. Gate Assignment Problem

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

  10. GATES Constraints • Constraints without exceptions • Gate size • Constraints with exceptions • International versus domestic flights • Constraints with changing tolerances • Turn-around times

  11. GATES Constraints • Guidelines • Taxiway congestion • Convenience constraints • Time between flights • Distance between connecting flights

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

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

  14. Gate Assignment Benefits (Cont.) • Managers can review impact of changes, implement changes (i.e., what-if analysis). • ES integrated into airlines' major operations / scheduling systems through direct electronic interfaces, thus expediting scheduling.

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

  16. AI Branches • Natural Language • Robotics • Perceptive Systems • Expert Systems • Intelligent Machines

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

  18. Human Processing Capabilities • Insight: • creativity • create alternatives • chess game • perspicuous grouping

  19. Perspicuous Grouping • Recognize that we can handle only a few alternatives • Short Term Memory (STM) • Miller’s 7 +/- 2 Rule • Zero in on a few viable alternatives • Enumerate / select best • Satisficing, rather than optimizing • Herbert Simon’s 1958 Chess prediction

  20. Computer Processing Capabilities • Handle large volume of data • quickly • Detect signals where humans sense “noise” • Tireless

  21. Computer Capabilities • Consistent • Objective • no “selective perception” • Not distracted • Minimal “down-time”

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

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

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

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

  26. Check Overdraft Cases Decision Attributes Decision Overdraft for Single or Multiple Checks Pay or Reject Type of Account Credit Rating Pay Regular Good Multiple Pay Student Unknown Single Reject Student Poor Single Reject Student Good Multiple Pay Student Good Single

  27. Check Overdraft Cases (Cont.) Decision Attributes Decision Overdraft for Single or Multiple Checks Pay or Reject Type of Account Credit Rating Pay Regular Unknown Multiple Pay Regular Good Single Reject Regular Poor Single Reject Student Unknown Multiple Reject Regular Unknown Multiple

  28. Pay or Reject? Overdraft for Single or Multiple Checks Pay or Reject Type of Account Credit Rating ? Regular Unknown Single

  29. Bank Overdraft Application • 340 Cases of check overdrafts • Classification Variable: • Check unpaid(0) or paid (1)

  30. ID3 DECISION TREE CR *DIFF<6.5 176 Yes No 130 CR*DIFF<5.5 DIFF<10.5 60 116 125 5 Yes No Yes No CR *DIFF<.035 DIFF<40.3 DIFF<9.4 DIFF<20.5 59 1 15 101 57 68 4 1 ACT*DIFF COV*DIFF<1.5 DIFF<42.2 DIFF<1.65 <19.6 0 14 1 69 50 9 1 32 53 2 2 0 1 56 15 1 DIFF<5.55 Pay Reject Pay Reject ACT*DIFF<.175 48 4 1 0 5 2 32 0 0 0 0 15 56 1 0 1 Reject Reject Reject Pay Reject Pay 2 3 0 ACT*DIFF<3 2 0 54 1 2 Overall Classification Rate: 97.7% Pay Reject Pay 2 0 1 1 Reject Pay

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

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

  33. ES Limitations • High development cost • Limited to relatively simple problems • operational mgmt level • Can be difficult to use • Can be difficult to maintain

  34. When to Use ES • High potential payoff OR • Reduced risk • Need to replace experts • Campbell’s Soup

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

  36. ES Versus DSS • Problem Structure: • ES: structured problems • clear • consistent • unambiguous • DSS: semi-structured problems

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

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

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

  40. MYACIN • Built ES with rules based on panel consensus • 68% accuracy • Why better than doctors? • Heuristics

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

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

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

  44. 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)

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

  46. 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)

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

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

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