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EXPERT SYSTEMS

EXPERT SYSTEMS. Review – Classical Expert Systems. Can incorporate Neural, Genetic and Fuzzy Components. Expert Systems can perform many functions. Rules can be fuzzy, quantum, modal, neural, Bayesian, etc. Special inference methods may be used. Concepts of Knowledge Representation:

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EXPERT SYSTEMS

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  1. EXPERT SYSTEMS

  2. Review – Classical Expert Systems • Can incorporate Neural, Genetic and Fuzzy Components

  3. Expert Systems can perform many functions

  4. Rules can be fuzzy, quantum, modal, neural, Bayesian, etc. • Special inference methods may be used

  5. Concepts of Knowledge Representation: INFERENCE

  6. Inference versus Knowledge Representation

  7. Real-Time Implementation of Rule-Based Control System. Handelman and Stengel 1989

  8. High level control logic • High level reconfiguration logic

  9. Inference can be fuzzy • Inference can use neural net • Inference can be based on search • Inference can be probabilistic • Inference can use higher-order-logic Any system, including a robot, can be made self-checking, fault – tolerant and reconfigurable

  10. Concepts of Knowledge Representation: DATA

  11. Animal Decision Tree: Example

  12. PROGRAMMING LANGUAGES

  13. PROGRAMMING LANGUAGES versus RULES

  14. Conclusions • Expert Systems can be used in conjunction with Neural Nets, Evolutionary Algorithms and all other kinds of problem-solving/learning mechanisms. • Standard classical programming can be smoothly interfaced with Training and Evolving and Uncertainty based philosophies like in Evolutionary, Neural and Fuzzy methodologies.

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