Intelligent Support 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 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 Blues human programmers included chess
  master

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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

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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