Managing Multiple Case Bases: Dimensions and Issues.
David B. Leake and Raja Sooriamurthi.
Proceedings of the Fifteenth International Florida Artificial
Intelligence Research Society Conference. AAAI Press, Menlo
Park, 2001, pp. 106-110. 5 pages.
Case-based reasoning (CBR) models the process of reasoning from
specific experiences acquired by an agent, and contained in the
agent's case-base. When multiple agents acquire cases, opportunities
arise for sharing their case-bases, with accompanying issues for how
to apply others' experiences effectively. This paper examines issues
for multi-case-base reasoning (MCBR), the reasoning process needed for
a CBR system to exploit external case-bases reflecting similar but
different tasks and task environments. The paper summarizes the
component processes required, the dimensions along which these
processes may differ, and some of the key research issues that must be
addressed for successful MCBR systems. It closes with a perspective
on the relationships of case-based reasoning and multi-case-base
reasoning, examining the analogy between reasoning about cases in CBR
and case-bases in MCBR.
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