Integrating case- and rule-based reasoning

Abstract

Rule-based reasoning (RBR) and case-based reasoning (CBR) have emerged as two important and complementary reasoning methodologies in artificial intelligence (AI). For problem solving in complex, real-world situations, it is useful to integrate RBR and CBR. This paper presents an approach to achieve a compact and seamless integration of RBR and CBR within the base architecture of rules. It is shown that the integration of CBR and RBR is possible without altering the inference engine of RBR. The paper focuses on the possibilistic (interpreted on the basis of similarity) nature of the approximate reasoning methodology common to both CBR and RBR. In CBR, the concept of similarity is cast as the complement of the distance between cases. In RBR the transitivity of similarity is the basis for the approximate deductions based on the generalized modus ponens. Approximate reasoning under uncertainty is also incorporated into the integration and is useful for dealing with many real-life situations and providing a comprehensive representation for CBR. This integration is illustrated in the financial domain of mergers and acquisitions. These ideas have been implemented in a prototype system, called a Mergers and Acquisitions Reasoning System (MARS).