David B. Leake, Andrew Kinley, and David Wilson. Proceedings of
the Fourteenth National Conference on Artificial Intelligence,
AAAI Press, Menlo Park, CA, 1997. 6 pages.
Abstract
Case-based problem-solving systems rely on {\it similarity assessment}
to select stored cases whose solutions are easily {\it adaptable} to
fit current problems. However, widely-used similarity assessment
strategies, such as evaluation of semantic similarity, can be poor
predictors of adaptability. As a result, systems may select cases
that are difficult or impossible for them to adapt, even when easily
adaptable cases are available in memory. This paper presents a new
similarity assessment approach which couples similarity judgments
directly to a case library containing the system's adaptation
knowledge. It examines this approach in the context of a case-based
planning system that learns both new plans and new adaptations.
Empirical tests of alternative similarity assessment strategies show
that this approach enables better case selection and increases the
benefits accrued from learned adaptations.
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