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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