Integrating Case-Based and Rule-Based Reasoning: the Possibilistic Connection

TL;DR

This paper proposes a seamless integration of rule-based and case-based reasoning in AI by leveraging their shared possibilistic approximate reasoning, demonstrated through a prototype system in financial decision-making.

Contribution

It introduces a novel method to combine RBR and CBR within a unified framework without modifying the core inference engine, utilizing their common possibilistic reasoning approach.

Findings

Successful implementation in a financial domain

Maintains the integrity of the RBR inference engine

Demonstrates effective integration of reasoning methodologies

Abstract

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.