Optimal case-based refinement of adaptation rule bases for engineering design

Rule-based systems have been successfully applied for adaptation. But the rule-based adaptation knowledge for engineering design has no static characteristic. Therefore the adaptation problem emerges also as a validation and refinement problem to be solved by global CBR approaches in an optimal way. The optimal refinement of engineering rule bases for adaptation improves the performance of expert systems for engineering design and provides a basis for the revision of the similarity function for the adaptation-guided retrieval. However, selecting optimal rule refinements is an unsolved problem in CBR; the employed classical SEEK2-like hill-climbing procedures yield local maxima only, not global ones. Hence for the cased-based optimization of rule base refinement a new operations research approach to the optimal selection of normal, conflicting, and alternative rule refinement heuristics is presented here. As the current rule validation and rule refinement systems usually rely on CBR, this is a relevant novel contribution for coping with the maintenance problem of large CBR systems for engineering design. The described global mathematical optimization enables a higher quality in the case-based refinement of complex engineering rule bases and thereby improves the basis for the adaptation-guided retrieval.