A Case Study of Case-Based CBR*

David B. Leake, Andrew Kinley, and David Wilson

Computer Science Department
Lindley Hall 215, Indiana University
Bloomington, IN 47405, U.S.A.
{leake ,akinley,davwils}@cs.indiana.edu



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.
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