Preference-Based CBR: General Ideas and Basic Principles

Building on recent research on preference handling in artificial intelligence and related fields, our goal is to develop a coherent and generic methodological framework for case-based reasoning (CBR) on the basis of formal concepts and methods for knowledge representation and reasoning with preferences. A preference-based approach to CBR appears to be appealing for several reasons, notably because case-based experiences naturally lend themselves to representations in terms of preference or order relations. Moreover, the flexibility and expressiveness of a preference-based formalism well accommodate the uncertain and approximate nature of case-based problem solving. In this paper, we outline the basic ideas of preferencebased CBR and sketch a formal framework for realizing these ideas.

Authors

• Eyke Hüllermeier Ludwig-Maximilians-Universität München Munich Center of Machine Learning German Research Center for Artificial Intelligence
• Weiwei Cheng

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