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Adaptation-guided Retrieval: Questioning the Similarity Assumption in Reasoning by Barry Smyth; Mark T. Keane is a Computer Science article available to read on EtoBox.

What is Adaptation-guided Retrieval: Questioning the Similarity Assumption in Reasoning about?

One of the major assumptions in Artificial Intelligence is that similar experiences can guide future reasoning, problem solving and learning; what we will call, the similarity assumption. The similarity assumption is used in problem solving and reasoning systems when target problems are dealt with by resorting to a previous situation with common conceptual features. In this article, we question this assumption in the context of case-based reasoning (CBR). In CBR, the similarity assumption plays a central role when new problems are solved, by retrieving similar cases and adapting their solutions. The success of any CBR system is contingent on the retrieval of a case that can be successfully reused to solve the target problem. We show that it is often unwarranted to assume that the most similar case is also the most appropriate from a reuse perspective. We argue that similarity must be augmented by deeper, adaptation knowledge about whether a case can be easily modified to fit a target problem. We implement this idea in a new technique, called adaptation-guided retrieval (AGR), which provides a direct link between retrieval similarity and adaptation needs. This technique uses special

Who reads Adaptation-guided Retrieval: Questioning the Similarity Assumption in Reasoning?

Author

Barry Smyth; Mark T. Keane

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0004-3702)

Published

1998

Language

EN

Field

Computer Science (Physical Sciences)