Formalising the Knowledge Content of Case Memory Systems

Discussions of case-based reasoning often reflect an implicit assumption that a case memory system will become better informed, i.e. will increase in knowledge, as more cases are added to the case-base. This paper considers formalisations of this ‘knowledge content’ which are a necessary preliminary to more rigourous analysis of the performance of case-based reasoning systems. In particular we are interested in modelling the learning aspects of case-based reasoning in order to study how the performance of a case-based reasoning system changes as it accumulates problem-solving experience. The current paper presents a ‘case-base semantics’ which generalises recent formalisations of case-based classification. Within this framework, the paper explores various issues in assuring that these semantics are well-defined, and illustrates how the knowledge content of the case memory system can be seen to reside in both the chosen similarity measure and in the cases of the case-base.