Abstract
Building on recent research on preference handling in artificial intelligence and related fields, our general goal is to develop a coherent and universally applicable methodological framework for 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 relations, even when not dealing with preference information in a literal sense. 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 make a first step toward a preference-based formalization of CBR. Apart from providing a general outline of the framework as a whole, we specifically address the step of case-based inference. The latter consists of inferring preferences for candidate solutions in the context of a new problem, given such preferences in similar situations. Our case-based approach to predicting preference models is concretely realized for a scenario in which solutions are represented in the form of subsets of a reference set. First experimental results are presented to demonstrate the effectiveness of this approach.
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Hüllermeier, E., Schlegel, P. (2011). Preference-Based CBR: First Steps toward a Methodological Framework. In: Ram, A., Wiratunga, N. (eds) Case-Based Reasoning Research and Development. ICCBR 2011. Lecture Notes in Computer Science(), vol 6880. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23291-6_8
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DOI: https://doi.org/10.1007/978-3-642-23291-6_8
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