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Chapter 11 Intelligent Support Systems Agenda • • • • Artificial Intelligence Expert Systems (ES) Differences between ES and DSS ES Examples Artificial Intelligence • Effort to develop computer-based systems that behave like humans: – – – – Learn languages Accomplish physical tasks Use a perceptual apparatus Emulate human thinking AI Branches • • • • • • Natural language Robotics Vision systems Expert systems Intelligent machines Neural network Agenda • • • • Artificial Intelligence Expert Systems (ES) Differences between ES and DSS ES Examples 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” Components of an Expert System • • • • • • • Knowledge acquisition facility Knowledge base (fact and rule) Inference engine User interface Explanation facility Recommended action User 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 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 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 Agenda • • • • Artificial Intelligence Expert Systems (ES) Differences between ES and DSS ES Examples ES Versus DSS • Problem Structure: – ES: structured problems • • • • clear consistent unambiguous limited scope – DSS: semi-structured problems ES Versus DSS • Quantification: – DSS: quantitative – ES: non-mathematical reasoning IF A BUT NOT B, THEN Z • Purpose: – DSS: aid manager – ES: replace manager Agenda • • • • Artificial Intelligence Expert Systems (ES) Differences between ES and DSS ES Examples Deep Blue • World chess champion Gary Kasparov • IBM chess computer “Deep Blue” • 1997 match • Deep Blue’s human programmers included chess master 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 MYACIN • Diagnose patient symptoms (triage) – Free doctors for high-level tasks • Panel of doctors – Diagnose sets of symptoms – Determine causes – 62% accuracy MYACIN • Built ES with rules based on panel consensus • 68% accuracy 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 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 Stock Market ES • Rule-based ES built • Discovered that only 15 data elements needed • Refined the ES model • Results were better than expert Points to Remember • • • • Artificial Intelligence Expert Systems (ES) Differences between ES and DSS ES Examples 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? Assignment • • • • Review chapters 7-11 Read chapter 12 Group assignment Research paper