Multi-case-base reasoning (MCBR) extends case-based reasoning to draw on multiple case bases that may address somewhat different tasks. In MCBR, an agent selectively supplements its own case-base as needed, by dispatching problems to external case-bases and using cross-case-base adaptation to adjust their solutions for inter-case-base differences. MCBR is often advocated as a means to facilitate handling large case-bases, or to enable use of distributed case sources. However, this raises an important question: When storage is not an issue, and the entire external case-base is available, is there any reason for MCBR? This paper answers that question with an experimental assessment of how MCBR affects the quality of solutions generated. It demonstrates that for a given local case-base and an external case-base for a task environment that is similar to, but different from, the local task environment, MCBR can improve accuracy compared to merging the case-bases into a single case-base. This improvement holds even if the cross-case-base adaptation method used by MCBR is also applied to the external cases before merging. The paper hypothesizes an explanation of this behavior in terms of the ability of MCBR to exploit the tradeoffs between similarity of problems and similarity of solution contexts. It provides experimental evidence to support this hypothesis, and also demonstrates that MCBR is a useful framework for selecting cases to add to a case-base.
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