Knowledge acquisition and refinement in expert systems
In general, an expert system is composed of a knowledge base that stores application-specific reasoning knowledge, an inference engine that processes the stored knowledge, and an interface through which communication links between the users and the expert system are established. In terms of knowledge refinement, this architecture is dependent on knowledge engineers to refine its stored knowledge on a periodical basis. The frequency with which the knowledge base is revised depends very much on the underlying application domain. This thesis generalizes the principle of knowledge acquisition to knowledge refinement of a continuous nature. While knowledge acquisition takes place in the early stage of expert-system development, knowledge refinement is applicable during its entire life span. A conceptual framework of building knowledge acquisition systems is first presented. Based on this framework, a generic architecture of expert systems with a provision to refine its own knowledge base is discussed.
- Research Organization:
- Purdue Univ., Lafayette, IN (USA)
- OSTI ID:
- 5503409
- Country of Publication:
- United States
- Language:
- English
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