Integrating Case-Based and Rule-Based Reasoning: the Possibilistic Connection
This work addresses the need for combined reasoning methodologies in AI for practical applications, but it appears incremental as it builds on existing paradigms without major breakthroughs.
The paper tackles the problem of integrating rule-based reasoning (RBR) and case-based reasoning (CBR) for complex real-world problem-solving by proposing an approach that achieves a compact and seamless integration within the base architecture of rules, illustrated in the financial domain of mergers and acquisitions with a prototype system called MARS.
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.