Case-based similarity assessment: estimating adaptability from experience

Authors:
David B. Leake;Andrew Kinley;David Wilson
Affiliations:
Computer Science Department, Indiana University, Bloomington, IN;Computer Science Department, Indiana University, Bloomington, IN;Computer Science Department, Indiana University, Bloomington, IN
Venue:
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Year:
1997

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Abstract

Case-based problem-solving systems rely on similarity assessment to select stored cases whose solutions are easily 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.