Case Retrieval Nets: Foundations, Properties, Implementation, and Results

An e cient retrieval of a relatively small number of relevant cases from a huge case base is a crucial subtask of Case-Based Reasoning (CBR). In this report, the framework of Case Retrieval Nets is presented. The main idea of these nets is to construct a memory structure representing both, the contents of the case base, and the knowledge about similarity in the domain considered. Then, a spreading activation process can be utilized to retrieve cases being su ciently similar to a posed query case. This report covers formal descriptions, investigation of properties, matters of implementation, and empirical results.