Understanding Artificial Intelligence and Expert Systems

1. Chapter 11 Intelligent Support Systems

2. Agenda • Artificial Intelligence • Expert Systems (ES) • Differences between ES and DSS • ES Examples

3. Artificial Intelligence • Effort to develop computer-based systems that behave like humans: • Learn languages • Accomplish physical tasks • Use a perceptual apparatus • Emulate human thinking

4. AI Branches • Natural language • Robotics • Vision systems • Expert systems • Intelligent machines • Neural network

5. Agenda • Artificial Intelligence • Expert Systems (ES) • Differences between ES and DSS • ES Examples

6. ES • Feigenbaum “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”

7. Components of an Expert System • Knowledge acquisition facility • Knowledge base (fact and rule) • Inference engine • User interface • Explanation facility • Recommended action • User

8. Reasons For Using ES • Consistent • Never gets bored or overwhelmed • Replaces absent, scarce experts • Quick response time • Cheaper than experts • Integration of multi-expert opinions • Eliminate routine or unsatisfactory jobs for people

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

10. When to Use ES • High potential payoff • Reduced risk • Need to replace experts • Need more consistency than humans • Expertise needed at various locations at same time • Hostile environment dangerous to human health

11. Agenda • Artificial Intelligence • Expert Systems (ES) • Differences between ES and DSS • ES Examples

12. ES Versus DSS • Problem Structure: • ES: structured problems • clear • consistent • unambiguous • limited scope • DSS: semi-structured problems

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

14. Agenda • Artificial Intelligence • Expert Systems (ES) • Differences between ES and DSS • ES Examples

15. Deep Blue • World chess champion Gary Kasparov • IBM chess computer “Deep Blue” • 1997 match • Deep Blue’s human programmers included chess master

16. Deep Blue • Included database that plays endgame flawlessly • 5 or fewer pieces on each side • Can Deep Blue calculate possibilities of earlier play? • Kasparov lost - became frustrated and played poorly

17. MYACIN • Diagnose patient symptoms (triage) • Free doctors for high-level tasks • Panel of doctors • Diagnose sets of symptoms • Determine causes • 62% accuracy

18. MYACIN • Built ES with rules based on panel consensus • 68% accuracy

19. 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

20. Stock Market ES • 50 data elements found • Reduced to 30 • Redundancy • Not really used • Undependable • Predicted for 6 months of data whether stock value would increase, decrease, or stay the same

21. Stock Market ES • Rule-based ES built • Discovered that only 15 data elements needed • Refined the ES model • Results were better than expert

22. Points to Remember • Artificial Intelligence • Expert Systems (ES) • Differences between ES and DSS • ES Examples

23. Discussion Questions • What do you think about the following statement? • “Expert systems are dangerous. People are likely to be dependent on them rather than think for themselves.” • What kind of ES does your organization have? • What kind of ES will benefit your organization?

24. Assignment • Review chapters 7-11 • Read chapter 12 • Group assignment • Research paper