Case-based reasoning (CBR) has received a great deal of attention
in recent years and has established itself as a core methodology in
the field of artificial intelligence. The key idea of CBR is to
tackle new problems by referring to similar problems that have
already been solved in the past. More precisely, CBR proceeds from
individual experiences in the form of cases. The generalization
beyond these experiences typically relies on a kind of regularity
assumption demanding that 'similar problems have similar
solutions'. Making use of different frameworks of approximate
reasoning and reasoning under uncertainty, notably probabilistic
and fuzzy set-based techniques, this book develops formal models of
the above inference principle, which is fundamental to CBR. The
case-based approximate reasoning methods thus obtained especially
emphasize the heuristic nature of case-based inference and aspects
of uncertainty in CBR.
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