Abstract
For the artificial intelligence community the competence or coverage of a given problem solving system is a fundamental evaluation criterion. This is of course also true for case-based reasoning (CBR) systems, which solve new problems by reusing and adapting the solutions of similar problems stored as cases in a case-base [3,4,12]. However, our current understanding of the competence issue is limited. Usually the competence of a system is demonstrated empirically, for example, by measuring the percentage problem solving success of a system. Unfortunately, this type of approach reveals little more that the tip of the iceberg when it comes to the true nature of competence in CBR. For example, it reveals nothing about the underlying source of competence for a given system, and carries only limited predictive power. This makes it impossible to determine how the competence of a given system will vary under different run-time conditions (e.g., changes in the case-base, adaptation knowledge, or target problem space).
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Smyth, B. (2000). Competence Models and Their Applications. In: Blanzieri, E., Portinale, L. (eds) Advances in Case-Based Reasoning. EWCBR 2000. Lecture Notes in Computer Science, vol 1898. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44527-7_1
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DOI: https://doi.org/10.1007/3-540-44527-7_1
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