COMBINATION OF CASE-BASED REASONING AND MULTI-ATTRIBUTE UTILITY THEORY IN LEGAL EXPERT SYSTEMS

Abstract

Case-Based Reasoning (CBR) has become a relevant alternative to the classical rule-based approach in expert systems because it gives valuable information about the current problem by comparing it to previously analysed problems. CBR, however, does not make superfluous the analysis of problems in themselves. This paper presents a novel framework called Case-Based Decision Making (CBDM), which is a special combination of CBR and Multi-Attribute Utility Theory (MAUT). The framework is applied to simulate judges' legal decision making by modelling case law and the 'doctrine of precedent'. First, the current decision problem is transformed into a decision matrix with two columns which is compared to matrices generated from previous problems, and we measure the distances between them. Finding a suitable distance measure is crucial. Decision, however, is not only based on nearness, but we also consider preference relations on alternatives and cases. Finally, global similarity between cases is defined from distance and preference. The technique can be used for any decision problem in which the number of alternatives can be reduced to two. The existence of a 'case-base' filled with previously evaluated problems is essential. The model has been implemented in a spreadsheet-based computer program, DEBORAH, that operates as a decision support tool allowing the user to set optional measures and functions for experimentation.