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Fuzzy set modelling in case‐based reasoning
Author(s) -
Dubois Didier,
Prade Henri,
Esteva Francesc,
Garcia Pere,
Godo Lluís,
López de Màntaras Ramon
Publication year - 1998
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/(sici)1098-111x(199804)13:4<345::aid-int3>3.0.co;2-n
Subject(s) - computer science , artificial intelligence , case based reasoning , reasoning system , set (abstract data type) , opportunistic reasoning , deductive reasoning , inference , outcome (game theory) , model based reasoning , rule of inference , similarity (geometry) , qualitative reasoning , fuzzy logic , fuzzy set , machine learning , mathematics , knowledge representation and reasoning , mathematical economics , image (mathematics) , programming language
This paper is an attempt at providing a fuzzy set formalization of case‐based reasoning and decision. Learning aspects are not considered here. The proposed approach 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. A particular instance of case‐based reasoning, named case‐based decision, is especially investigated. A logical formalization of the basic case‐based reasoning inference is also proposed. Extensions of the proposed approach in order to handle imprecise or fuzzy descriptions or to manage more general forms of the principle underlying case‐based reasoning are briefly discussed in the conclusion. © 1998 John Wiley & Sons, Inc.