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MODELING AND INTELLIGENT DSS

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Combined problem solving - qual/quant. Intelligent backend - results interpretation ... Expert systems, neural nets, fuzzy sets, genetic algorithms, natural language ... – PowerPoint PPT presentation

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Title: MODELING AND INTELLIGENT DSS


1
MODELING AND INTELLIGENT DSS
  • IS3301 Session 4b
  • Prof. Mark Nissen

2
Agenda
  • Decision Modeling Examples
  • Dalian Dyestuff Plant
  • Intelligent DSS
  • Decision Exercise
  • Summary

3
Decision Modeling Examples
  • Influence diagram - work route
  • Software contractor selection
  • Where does intelligence reside?
  • What decisions does LDW make?
  • What role could AI play?
  • How to model knowledge?

4
Dalian Dyestuff Plant
  • Dalian Dyestuff
  • Largest chemical producer in China
  • Chinese economic reform DM?
  • Intelligent DSS
  • Help Plant managers - production planning
  • Optimization functions?
  • Expert system functions?
  • 10 profit increase - how?
  • Qualitative quantitative analysis?

5
Intelligent DSS
  • Natural vs. machine intelligence?
  • AI DSS applications
  • Intelligent frontend - user friendly
  • Combined problem solving - qual/quant
  • Intelligent backend - results interpretation
  • DSS agent - delegate decision making
  • Technologies
  • Expert systems, neural nets, fuzzy sets, genetic
    algorithms, natural language processing, vision,
    robotics, others

6
Military Expert Systems
  • Turbine Engine Diagnostics
  • M1tank engine domain - why?
  • Hard problem? Why?
  • Expert system approach - why?
  • Results?
  • Aeromedical Evacuation
  • Aeromedical evacuation domain - why?
  • Hard problem? Why?
  • Consistency planner approach - why?
  • Results?

7
Decision Exercise
  • Network-upgrade decision
  • Form teams of 3
  • Ten minutes
  • Brief class
  • Exercise
  • Use LDW to address problem?
  • How to model problem-solving knowledge?

8
Summary
  • Models support I problem solving
  • Knowledge resides with human
  • Intelligent DSS
  • Raise IQ of DSS
  • Help overcome user ignorance
  • Integration of AI with other tools
  • Characteristics of intelligence
  • Human vs. machine differences
  • Several classes of IS use intelligence
  • Knowledge modeling in expert systems
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