Abstract
For certain application areas of case-based reasoning, the traditional view of cases as points in the problem-solution space is not appropriate. Motivated by a concrete application in the area of electronic design reuse, we introduce the concept of a generalized case that represents experience that naturally covers a space rather than a point. Within a formal framework we introduce the semantics of generalized cases and derive a canonical similarity measure for them. Generalized cases can be represented in a very flexible way by using constraints. This representation asks for new means of similarity assessment. We argue that in principle fuzzy constraint satisfaction or non-linear programming can be applied for similarity computation. However, to avoid the computational complexity of these approaches, we propose an algorithm for an efficient estimation of similarity for generalized cases.
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Bergmann, R., Vollrath, I. (1999). Generalized Cases: Representation and Steps Towards Efficient Similarity Assessment. In: Burgard, W., Cremers, A.B., Cristaller, T. (eds) KI-99: Advances in Artificial Intelligence. KI 1999. Lecture Notes in Computer Science(), vol 1701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48238-5_16
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DOI: https://doi.org/10.1007/3-540-48238-5_16
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