Fuzzy Modelling of Case-Based Reasoning
and Decision

Didier Dubois1, Francesc Esteva2, Pere Garcia2, Llus Godo2,
Ramon L. de Mntaras2 and Henri Prade1

1 Institut de Recherche en Informatique de Toulouse (IRIT), Universit Paul
Sabatier, Bt. 1R3, 118 Route de Narbonne, 31062 Toulouse Cedex 4, France
emails: {dubois ,prade}@irit.fr
2 Institut dInvestigaci en Intelligncia Artificial (IIIA), Consejo Superior de
Investigaciones Cientficas(CSIC), Campus Universitat Autonoma de Barcelona
08193 Bellaterra, Spain
emails: {esteva,pere,godo,mantaras}@iiia.csic.fr


Abstract. 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 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 precedent
cases to the current problem. A particular instance of case-based reasoning, 
named case-based decision, is especially investigated. A logical
model of case-based inference is also described.
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