Abstract

Assuming that adaptation knowledge will continue to be an important part of CBR systems, a major challenge for the area is to overcome the knowledge-engineering problems that arise in its acquisition. This paper describes an approach to automating the acquisition of adaptation knowledge overcoming many of the associated knowledge-engineering costs. This approach makes use of inductive techniques, which learn adaptation knowledge from case comparison. We also show how this adaptation knowledge can be usefully applied and report on how available domain knowledge might be exploited in such an adaptation-rule learning-system. 1. Introduction Case-based reasoning systems solve problems by reusing the solutions to previously solved problems. Typically, there are differences between a new problem and the previous case so the success of a CBR system often critically depends on its ability to adapt the solution of a previous case to suit a new situation. Up to the time of writing, the a...

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