Abstract
Case-based reasoning systems solve new problems by reusing previous problem solving experience stored as cases in a case-base. In recent years the maintenance problem has become an increasingly important research issue for the case-based reasoning community. In short, the goal is to develop strategies for effectively maintaining the efficiency and competence of case-based reasoning systems as they evolve. Our research has focused on the development of a model of competence for case-based reasoning systems, a model that measures the contributions of individual cases to overall system competence, and which forms the computational basis for a variety of maintenance strategies. However, while this model offers many potential advantages its upkeep adds an additional cost to the CBR cycle. In this paper we evaluate a new method for more efficiently updating the model at run-time.
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Smyth, B., McKenna, E. (2000). An Efficient and Effective Procedure for Updating a Competence Model for Case-Based Reasoners. In: López de Mántaras, R., Plaza, E. (eds) Machine Learning: ECML 2000. ECML 2000. Lecture Notes in Computer Science(), vol 1810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45164-1_37
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DOI: https://doi.org/10.1007/3-540-45164-1_37
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