Introduction To the Special Issue on Maintaining Case‐Based Reasoning Systems

Case-based reasoning (CBR) is the process of reasoning and learning by storing prior cases—records of specific prior reasoning episodes—and retrieving and adapting them to aid new problem solving or interpretation in similar situations (Kolodner 1993, Leake 1996, Watson 1997). Case-based reasoning systems rely on the knowledge contained in multiple “knowledge containers” (Richter 1998), such as the case base, case adaptation knowledge, and similarity criteria. The contents of each of these knowledge containers may affect system efficiency and the quality of results. Over time, the knowledge containers may need to be updated in order to maintain or improve performance in response to changes in the system’s knowledge, task, environment, or user base. This gives rise to the need for strategies to address the problem of maintenance in case-based reasoning systems. Experience with the growing number of deployed case-based reasoning systems has led to increasing recognition of the value of maintenance to the success of practical CBR systems, as well as the importance of maintenance research. Maintenance issues arise in designing and building CBR systems and support tools that monitor system state and effectiveness in order to determine whether, when, and how to update CBR system knowledge to better serve performance goals. Understanding the issues that underlie the maintenance problem and using that understanding to develop good practical maintenance strategies are crucial to sustaining and improving the efficiency and solution quality of CBR systems as their case bases grow and as their tasks or environments change over long-term use. Maintaining CBR systems is an active research area that has been well represented at recent conferences. This special issue brings together mature work focusing on maintaining the essential underlying knowledge of case-based reasoning systems. It provides a snapshot of the state of the art, presenting twelve articles examining core issues, methods, and lessons learned from research and applications. Topics include the foundations of CBR system maintenance—the components of the maintenance process and maintenance goals—as well as proposals for specific maintenance strategies, theoretical and empirical analyses of their performance, and lessons on maintenance arising from practical experience. In order to understand the issues involved in developing maintenance strategies, as well as to understand maintenance practice and identify opportunities for new research, it is useful to understand the nature of the maintenance process and its relationship to the overall CBR process. The first article in this issue, Wilson and Leake’s “Maintaining Case-Based Reasoners: Dimensions and Directions,” provides a characterization of what maintenance is, the components of maintenance policies, and the dimensions along which alternative maintenance policies may differ. It then uses that characterization to examine the state of the art and identify opportunities for future research. Of course, the success of maintenance depends not only on the maintenance policies themselves but also on how maintenance is integrated with the overall case-based reasoning process. Reinartz, Iglezakis, and Roth-Berghofer’s article, “On Quality Measures for Case Base Maintenance,” describes an extended, six-step CBR cycle that incorporates two explicit