This paper presents an algorithm of adaptation for a case-based reasoning system with cases and domain knowledge represented in the expressive description logic ALC. The principle is to first pretend that the source case to be adapted solves the current target case. This may raise some contradictions with the specification of the target case and with the domain knowledge. The adaptation consists then in repairing these contradictions. This adaptation algorithm is based on an extension of the classical tableau method used for deductive inferences in ALC.

Adaptation has long been considered as the Achilles' heel of case-based reasoning since it requires some domain-specific knowledge that is difficult to acquire. In this paper, two strategies are combined in order to reduce the knowledge engineering cost induced by the adaptation knowledge (AK) acquisition task: AK is learned from the case base by the means of knowledge discovery techniques, and the AK acquisition sessions are opportunistically triggered, i.e., at problem-solving time.

In case-based reasoning, the adaptation of a source case in order to solve the target problem is at the same time crucial and difficult to implement. The reason for this difficulty is that, in general, adaptation strongly depends on domain-dependent knowledge. This fact motivates research on adaptation knowledge acquisition (AKA). This paper presents an approach to AKA based on the principles and techniques of knowledge discovery from databases and data-mining. It is implemented in CABAMAKA, a system that explores the variations within the case base to elicit adaptation knowledge. This system has been successfully tested in an application of case-based reasoning to decision support in the domain of breast cancer treatment.

In case-based reasoning, the adaptation step depends in general on domain-dependent knowledge, which motivates studies on adaptation knowledge acquisition (AKA). CABAMAKA is an AKA system based on principles of knowledge discovery from databases. This system explores the variations within the case base to elicit adaptation knowledge. It has been successfully tested in an application of case-based decision support to breast cancer treatment.