Abstract
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.
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Griffiths, A.D., Bridge, D.G. (1995). Formalising the knowledge content of case memory systems. In: Watson, I.D. (eds) Progress in Case-Based Reasoning. UK CBR 1995. Lecture Notes in Computer Science, vol 1020. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60654-8_20
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DOI: https://doi.org/10.1007/3-540-60654-8_20
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