Abstract
Case-based reasoning depends on multiple knowledge sources beyond the case library, including knowledge about case adaptation and criteria for similarity assessment. Because hand coding this knowledge accounts for a large part of the knowledge acquisition burden for developing CBR systems, it is appealing to acquire it by learning, and CBR is a promising learning method to apply. This observation suggests developing case-based CBR systems, CBR systems whose components themselves use CBR. However, despite early interest in case-based approaches to CBR, this method has received comparatively little attention. Open questions include how case-based components of a CBR system should be designed, the amount of knowledge acquisition effort they require, and their effectiveness. This paper investigates these questions through a case study of issues addressed, methods used, and results achieved by a case-based planning system that uses CBR to guide its case adaptation and similarity assessment. The paper discusses design considerations and presents empirical results that support the usefulness of case-based CBR, that point to potential problems and tradeoffs, and that directly demonstrate the overlapping roles of different CBR knowledge sources. The paper closes with general lessons about case-based CBR and areas for future research.
This work was supported in part by the National Science Foundation under Grant No. IRI-9409348.
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Leake, D.B., Kinley, A., Wilson, D. (1997). A case study of case-based CBR. In: Leake, D.B., Plaza, E. (eds) Case-Based Reasoning Research and Development. ICCBR 1997. Lecture Notes in Computer Science, vol 1266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63233-6_507
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DOI: https://doi.org/10.1007/3-540-63233-6_507
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