Integrating Feature Extraction and Memory Search

Authors:
Christopher Owens
Affiliations:
The University of Chicago, Department of Computer Science, 1100 E. 58th Street, Chicago, IL 60637. OWENS@CS.UCHICAGO.EDU
Venue:
Machine Learning - Special issue on case-based reasoning
Year:
1993

Citing 0
Cited 2

Book review: Case-Based Reasoning in Design by Mary Lou Maher, M. Bala Balachandran, and Dong Mei Zhang (Lawrence Erlbaum Associates, 1995)

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Abstract

Reasoning from prior cases or abstractions requires that a system identify relevant similarities between the current situation and objects represented in memory. Often, relevance depends upon abstract, thematic, costly-to-infer properties of the situation. Because of the cost of inference, a case-retrieval system needs to learn which descriptions are worth inferring, and how costly tht inference will be. This article outlines the properties that make an abstract thematic feature valuable to a case-based reasoner, and recasts the problem of case retrieval into a framework under which a system can explicitly and dynamically reason about the cost of acquiring features relative to their information value.