| Case-based reasoning (CBR) is an artificial intelligence methodology that uses specific encapsulated prior experiences as a basis for reasoning about similar new situations. CBR systems rely on various “knowledge containers,” such as the case-base of prior experiences and similarity criteria for comparing situations and retrieving the most relevant cases. Explicit or implicit changes in the reasoning environment, task focus, and user base may influence the fit of the current knowledge state to the task context, which can affect the quality and efficiency of reasoning results. Over time, the knowledge containers may need to be updated in order to maintain or improve performance in response to changes in task or environment. In particular, maintaining the case-base—the traditional mainstay of knowledge underlying CBR systems—is essential for preserving and expanding the capability of a CBR system throughout its life-cycle.; This dissertation provides a first coherent picture of the case-base maintenance problem in CBR and develops new case-base maintenance techniques within that paradigm. The thesis presents a theoretical framework for describing case-base maintenance techniques according to the types of maintenance policies implemented by a given system. The framework serves to unify current maintenance practice, to point out areas for new fundamental research, and as a step toward recommending the best maintenance practices for varying system performance goals.; The theoretical picture of case-base maintenance is then complemented with a presentation of new methods and experiments in applied case-base maintenance. DRAMA, a case-based tool for aerospace design support at NASA, provides facilities for initial case capture and subsequent refinement that directly exploit user knowledge. This helps to maintain the case-base through continuous support for case-authoring and design consistency while significantly ameliorating the knowledge-engineering burden on system users. Finally, the thesis examines new methods for automatically maintaining case-bases by incorporating explicit performance concerns into measures of case-base competence in order to optimize case-base composition. |
