Abstract
Case-based reasoning relies on the hypothesis that “similar problems have similar solutions,” which seems to apply, in a certain sense, to a large range of applications. In order to be generally applicable and useful for problem solving, however, this hypothesis and the corresponding process of case-based inference have to be formalized adequately. This paper provides a formalization which makes the “similarity structure” of a system accessible for reasoning and problem solving. A corresponding (constraint-based) approach to case-based inference exploits this structure in a way which allows for deriving a similarity-based prediction of the solution to a target problem in form of a set of possible candidates (supplemented with a level of confidence.)
This work has been supported by a TMR research grant funded by the European Commission.
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Hüllermeier, E. (1999). Exploiting Similarity for Supporting Data Analysis and Problem Solving. In: Hand, D.J., Kok, J.N., Berthold, M.R. (eds) Advances in Intelligent Data Analysis. IDA 1999. Lecture Notes in Computer Science, vol 1642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48412-4_22
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DOI: https://doi.org/10.1007/3-540-48412-4_22
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