Abstract
Case-based reasoning (CBR) systems solve new problems by retrieving stored prior cases, and adapting their solutions to fit new circumstances. Traditionally, CBR systems draw their cases from a single local case-base tailored to their task. However, when a system’s own set of cases is limited, it may be beneficial to supplement the local case-base with cases drawn from external case-bases for related tasks. Effective use of external case-bases requires strategies for multi-case-base reasoning (MCBR): (1) for deciding when to dispatch problems to an external case-base, and (2) for performing cross-case-base adaptation to compensate for differences in the tasks and environments that each case-base reflects. This paper presents methods for automatically tuning MCBR systems by selecting effective dispatching criteria and cross-case-base adaptation strategies. The methods require no advance knowledge of the task and domain: they perform tests on an initial set of problems and use the results to select strategies reflecting the characteristics of the local and external case-bases. We present experimental illustrations of the performance of the tuning methods for a numerical prediction task, and demonstrate that a small sample set can be sufficient to make high-quality choices of dispatching and cross-case-base adaptation strategies.
This research is supported in part by NASA under award No NCC 2-1216.
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Leake, D.B., Sooriamurthi, R. (2002). Automatically Selecting Strategies for Multi-Case-Base Reasoning. In: Craw, S., Preece, A. (eds) Advances in Case-Based Reasoning. ECCBR 2002. Lecture Notes in Computer Science(), vol 2416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46119-1_16
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DOI: https://doi.org/10.1007/3-540-46119-1_16
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