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. This way, the book contributes to a solid
foundation of CBR which is grounded on formal concepts and
techniques from the aforementioned fields. Besides, it establishes
interesting relationships between CBR and approximate reasoning,
which not only cast new light on existing methods but also enhance
the development of novel approaches and hybrid systems.
This books is suitable for researchers and practioners in the
fields of artifical intelligence, knowledge engineering and
knowledge-based systems.
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