Competence guided incremental footprint-based retrieval

摘要

Case-based reasoning (CBR) systems solve new problems by retrieving and adapting problem solving experiences stored as cases in a case-base. Success depends largely on the performance of the case retrieval algorithm used. Smyth and McKenna [Lecture Notes in Artificial Intelligence LNAI 1650 (1999) 343–357] have described a novel retrieval technique, called footprint-based retrieval (FBR), which is guided by a model of case competence. FBR as it stands benefits from superior efficiency characteristics and achieves near-optimal competence and quality characteristics. In this paper, we describe a simple but important extension to FBR. Empirically we show that this new algorithm can deliver optimal retrieval performance while at the same time retaining the efficiency benefits of the original FBR method.