A Context-Aware Approach to Selecting Adaptations for Case-Based Reasoning

Case-based reasoning solves new problems by retrieving cases of similar previously-solved problems and adapting their solutions to fit new circumstances. The case adaptation step is often done by applying context-independent adaptation rules. A substantial body of research has studied generating these rules automatically from comparisons of prior pairs of cases. This paper presents a method for increasing the context-awareness of case adaptation using these rules, by exploiting contextual information about the prior problems from which the rules were generated to predict their applicability to the context of the new problem, in order to select the most relevant rules. The paper tests the approach for the task of case-based prediction of numerical values (case-based regression). It evaluates performance on standard machine learning data sets to assess the method’s performance benefits, and also tests it on synthetic domains to study how performance is affected by different problem space characteristics. The results show the proposed method for context-awareness brings significant gains in solution accuracy.