Chapter 20 Expert System

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Chapter 20Expert System

323-670 Artificial Intelligence ??.??????
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Expert System

• p. 547 MYCIN (1976)
• see section 8.2
• backward chaining certainty factor and
  rule-based systems p.233
• Bayesian network p. 239
• Fuzzy logic p. 246
• Probability and Bayes theorem p. 231
• PROSPECTOR (1976), DENDRAL (1978)
• expert systems shells EMYCIN

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Expert System

• using domain knowledge
• knowledge representation p. 297
• reasoning with the knowledge, explanation
• Knowledge acquisition (p. 553)
• 1) entering knowledge
• 2) maintaining knowledge base consistency
• 3) ensuring knowledge base completeness
• MOLE (1988) is a knowledge acquisition system for
  heuristic classification problems such as
  diagnosing diseases.

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Expert System

• problem
• then number of rules may be large
• control structure depend on the specific
  characteristic of the problrem
• 1) Brittleness no general knowledge that can be
  used, the data is out of date
• 2) Lack of meta-knowledge the limitation of the
  control operation for reasoning
• 3) Knowledge acquisition difficult to transform
  the knowledge from human to machine
• 4) Validation the correctness of the knowledge
  in the system, no formal proof that machine is
  better than human or human better than machine.