A framework for retrieval in case-based reasoning systems

A case-based reasoning (CBR) system supports decision makers when solving new decision problems (i.e., new cases) on the basis of past experience (i.e., previous cases). The effectiveness of a CBR system depends on its ability to retrieve useful previous cases. The usefulness of a previous case is determined by its similarity with the new case. Existing methodologies assess similarity by using a set of domain-specific production rules. However, production rules are brittle in ill-structured decision domains and their acquisition is complex and costly. We propose a framework of methodologies based on decision theory to assess the similarity of a new case with the previous case that allows amelioration of the deficiencies associated with the use of production rules. An empirical test of the framework in an ill-structured diagnostic decision environment shows that this framework significantly improves the retrieval performance of a CBR system.