FROM ADAPTATION-GUIDED RETRIEVAL TO REUSE-GUIDED RETRIEVAL: APPLICATION TO CASE RETRIEVAL NET MEMORY MODEL
In Case-Based Reasoning (CBR), case retrieval is generally guided by similarity. However, the most similar case may not be the easiest one to reuse (hard to adapt). As recommended by Smyth and Keane, it might be more efficient to use an adaptability criterion to guide the retrieval process (adaptation-guided retrieval or AGR). In the same trend but with the goal of optimizing case reuse, our approach is to consider what is similar to copy and what is different to adapt during the retrieval stage. We introduce a more general framework for retrieval, namely the reuse-guided retrieval (RGR). The goal of this paper is twofold: first, it proposes a case retrieval approach that relies on reuse cost; 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 two case studies reveal that the proposed approach yields better recall quality than CRN's similarity only-based retrieval while having similar computational complexity.
adaptation-guided retrieval reuse-guided retrieval case retrieval case retrieval nets case based reasoning