  Rule based reasoning (RBR) and case based reasoning (CBR) have emerged as two
important and complementary reasoning methodologies in artificial intelligence
(Al). 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. The
paper focuses on the possibilistic nature of the approximate reasoning
methodology common to both CBR and RBR. In CBR, the concept of similarity is
casted 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. It is shown that the integration of CBR and RBR is
possible without altering the inference engine of RBR. This integration is
illustrated in the financial domain of mergers and acquisitions. These ideas
have been implemented in a prototype system called MARS.
