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