In case-based reasoning (CBR), problems are solved by retrieving prior cases and adapting their solutions to fit; learning occurs as new cases are stored. Controlling the growth of the case base is a fundamental problem, and research on case-base maintenance has developed methods for compacting case bases while maintaining system competence, primarily by competence-based deletion strategies assuming static case adaptation knowledge. This paper proposes adaptation-guided case-base maintenance (AGCBM), a case-base maintenance approach exploiting the ability to dynamically generate new adaptation knowledge from cases. In AGCBM, case retention decisions are based both on cases' value as base cases for solving problems and on their value for generating new adaptation rules. he paper illustrates the method for numerical prediction tasks (case-based regression) in which adaptation rules are generated automatically using the case difference heuristic. In comparisons of AGCBM to five alternative methods in four domains, for varying case base densities, AGCBM outperformed the alternatives in all domains, with greatest benefit at high compression.

The CBR special track at FLAIRS has come to fill the important role of a North American symposium on CBR and it is well regarded in the community. Ths year we were pleased to accept two full papers and one poster paper.

Conversational case-based reasoning systems use an interactive dialog to retrieve stored cases. Normally the ordering of questions in this dialog is chosen based only on their discriminativeness. However, because the user may not be able to answer all questions, even highly discriminative questions are not guaranteed to provide information. This paper presents a customization method CCBR systems can apply to adjust entropy-based discriminativeness considerations by predictions of user ability to answer questions. The method uses a naive Bayesian classifier to classify users into user groups based on the questions they answer, applies information from group profiles to predict which future questions they are likely to be able to answer, and selects the next questions to ask based on a combination of information gain and response likelihood. The method was evaluated for a mix of simulated user groups, each associated with particular probabilities for answering questions about each case indexing feature, in four sample domains. For simulated users with varying abilities to answer particular questions, results showed improvement in dialog length over a non-customized entropy-based approach in all test domains.