Automatic Refinement of Expert Systems (Research Notes in Artificial Intelligence) - Softcover

The research presented in this study aims to contribute to the development of useful knowledge base refinement systems both at the concrete level of system design, implementation and testing and also at the "meta-level" of development of tools and methodologies for pursuing research in this area. Relative to the former level, the thesis generalizes and extends the empirically-grounded heuristic approach to refinement generation developed by Politakis and Weiss, and analyzes strategies for using this approach in building automatic refinement systems. Relative to the level of tools and methodology, a high-level refinement metalanguage, RM, allowing for the specification of a wide variety of alternative refinement concepts, heuristics, and strategies has been designed and implemented. In addition to allowing for the growth of refinement systems by facilitating experimental research, RM also provides a means for refinement system customization and possible enhancement through the incorporation of domain-specific metaknowledge.

The incorporation of a formal metalanguage for knowledge base refinement represents an extension of the traditional model of an expert system framework, and is a step in the direction of more powerful, robust and self-improving expert system technology.