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 model of 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