Mark Winter and D. Sleeman

Abstract Case-Based Reasoning (CBR) is a paradigm which makes use of similarity to reuse previous problem solving incidents when solving a new problem. These stored problem solving incidents are known as cases and are stored in a case-base which acts as the main source of knowledge in a CBR system. The construction of a knowledge base typically involves three stages: eliciting the knowledge somehow, transforming this into the appropriate representation and refining (correcting) any errors in the resulting knowledge-base. Knowledge-base refinement systems (partially) automate this final stage, but the majority have dealt with rule-based representations. It is important for the successful use of CBR systems that their case-bases are consistent. To this end, this paper describes two knowledge refinement systems which refine case knowledge. REFINER deals with cases which have been classified by a domain expert and forms generalised descriptions of each of the categories which are used to detect and help remove inconsistent cases. The REFINER+ system addresses several shortcomings of REFINER but shares the same overall aims.