Fuzzy Rule-Based Models for Case-Based Reasoning

This paper is an attempt at providing a fuzzy set-based formalization of case-based reasoning. The proposed approach, which does not take into account the learning aspects of case-based reasoning, assumes a principle stating that "the more similar are the problem description attributes, the more similar are the outcome attributes". A weaker form of this principle concluding only on the graded possibility of the similarity of the outcome attributes, is also considered. These two forms of the case-based reasoning principle are modelled in terms of fuzzy rules. Then an approximate reasoning machinery taking advantage of this principle enables us to apply the information stored in the memory of previous cases to the current problem.