Case Retrieval with Combined Adaptability and Similarity Criteria: Application to Case Retrieval Nets

摘要

In Case Based Reasoning (CBR), case retrieval is generally guided by similarity. However, the most similar case may not be the easiest one to adapt, and it may be helpful to also use an adaptability criterion to guide the retrieval process. The goal of this paper is twofold: First, it proposes a method of case retrieval that relies simultaneously on similarity and adaptation costs. Then, it illustrates its use by integrating adaptation cost into the Case Retrieval Net (CRN) memory model, a similarity-based case retrieval system. The described retrieval framework optimizes case reuse early in the inference cycle, without incurring the full cost of an adaptation step. Our results on a case study reveal that the proposed approach yields better recall accuracy than CRN's similarity only-based retrieval while having similar computational complexity.