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Intelligent Support Systems

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					Intelligent Support Systems




             Prof. Rushen Chahal



                                   Page 1
               Agenda
•   Artificial Intelligence
•   Expert Systems (ES)
•   Differences between ES and DSS
•   ES Examples




                                     Page 2
     Artificial Intelligence
• Effort to develop computer-based
  systems
  that behave like humans:
  – Learn languages
  – Accomplish physical tasks
  – Use a perceptual apparatus
  – Emulate human thinking


                                     Page 3
             AI Branches
•   Natural language
•   Robotics
•   Vision systems
•   Expert systems
•   Intelligent machines
•   Neural network


                           Page 4
               Agenda

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




                                     Page 5
                 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”

                                       Page 6
     Components of an Expert System

•   Knowledge acquisition facility
•   Knowledge base (fact and rule)
•   Inference engine
•   User interface
•   Explanation facility
•   Recommended action
•   User

                                      Page 7
     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

                                          Page 8
          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



                                          Page 9
        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
                                      Page 10
               Agenda

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




                                     Page 11
           ES Versus DSS
• Problem Structure:
  – ES: structured problems
    •   clear
    •   consistent
    •   unambiguous
    •   limited scope
  – DSS: semi-structured problems



                                    Page 12
         ES Versus DSS
• Quantification:
  – DSS: quantitative
  – ES: non-mathematical reasoning
     IF A BUT NOT B, THEN Z
• Purpose:
  – DSS: aid manager
  – ES: replace manager



                                     Page 13
               Agenda
•   Artificial Intelligence
•   Expert Systems (ES)
•   Differences between ES and DSS
•   ES Examples




                                     Page 14
          Deep Blue


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

                                  Page 15
             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


                                      Page 16
               MYACIN

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



                                        Page 17
             MYACIN
• Built ES with rules based on panel
  consensus
• 68% accuracy




                                       Page 18
        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



                                       Page 19
        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

                                      Page 20
        Stock Market ES

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




                                    Page 21
       Points to Remember
•   Artificial Intelligence
•   Expert Systems (ES)
•   Differences between ES and DSS
•   ES Examples




                                     Page 22
    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?
                                           Page 23

				
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Description: Prof. Rushen's notes for MBA and BBA students