Learning to Improve Efficiency for Adaptation Paths

摘要

The ability of case-based reasoning systems to deal with new problems depends on the effectiveness of their case adaptation. One approach to increasing flexibility for novel problems is to perform adaptations by using adaptation paths-chains of adaptations-to address differences beyond those addressable by applying single adaptation rules. A recent approach to adaptation path generation, ROAD, proposes building adaptation paths using heuristic search guided by similarity, with a "reset" mechanism for recovering when similarity fails to predict adaptability. The ROAD approach is beneficial when similarity and adaptability are well aligned, but can make poor choices when similarity and adaptability diverge, increasing adaptation cost. This paper presents methods for increasing adaptation efficiency by maintenance exploiting information from adaptation path generation. The methods improve the similarity measure to better reflect adaptability and condense the adaptation rule set. Experimental evaluation supports the benefits for improving adaptation efficiency while preserving accuracy.