Automatically Selecting Strategies for Multi-Case-Base Reasoning
(pdf
)
David B. Leake and Raja Sooriamurthi.
ECCBR 2002: Advances in Case-Based Reasoning. Springer
Verlag, Berlin, 2002. In press. 15 pages.
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.
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