Automatically Selecting Strategies
for Multi-Case-Base Reasoning*

David B. Leake1 and Raja Sooriamurthi2

1 Computer Science Department, Indiana University, Lindley Hall 215
150 S. Woodlawn Avenue, Bloomington, IN 47405, U.S.A.
leake@cs.indiana.edu
2 Kelley School of Business, Indiana University, BU540,
1309 East 10th Street, Bloomington, IN 47405, U.S.A.
raja@indiana.edu



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 systems 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.
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