Exper_Machine Learning Experimental data.ppt
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Exper_Machine Learning Experimental data.ppt
- 2. 2 Expert System Main Components • Knowledge base – obtainable from books, magazines, knowledgeable persons, etc. • Inference engine – draws conclusions from the knowledge base
- 3. 3 Figure 1.2 Basic Functions of Expert Systems
- 4. 4 Problem Domain vs. Knowledge Domain • An expert’s knowledge is specific to one problem domain – medicine, finance, science, engineering, etc. • The expert’s knowledge about solving specific problems is called the knowledge domain. • The problem domain is always a superset of the knowledge domain.
- 5. 5 Figure 1.3 Problem and Knowledge Domain Relationship
- 6. 6 Foundation of Expert Systems
- 7. 7 General Methods of Inferencing • Forward chaining (data-driven)– reasoning from facts to the conclusions resulting from those facts – best for prognosis, monitoring, and control. – Examples: CLIPS, OPS5 • Backward chaining (query driven)– reasoning in reverse from a hypothesis, a potential conclusion to be proved to the facts that support the hypothesis – best for diagnosis problems. – Examples: MYCIN