Integrating Feature Extraction and Memory Search

摘要

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.