Belief rule-base inference methodology using the evidential reasoning Approach-RIMER

In this paper, a generic rule-base inference methodology using the evidential reasoning (RIMER) approach is proposed. Existing knowledge-base structures are first examined, and knowledge representation schemes under uncertainty are then briefly analyzed. Based on this analysis, a new knowledge representation scheme in a rule base is proposed using a belief structure. In this scheme, a rule base is designed with belief degrees embedded in all possible consequents of a rule. Such a rule base is capable of capturing vagueness, incompleteness, and nonlinear causal relationships, while traditional if-then rules can be represented as a special case. Other knowledge representation parameters such as the weights of both attributes and rules are also investigated in the scheme. In an established rule base, an input to an antecedent attribute is transformed into a belief distribution. Subsequently, inference in such a rule base is implemented using the evidential reasoning (ER) approach. The scheme is further extended to inference in hierarchical rule bases. A numerical study is provided to illustrate the potential applications of the proposed methodology.

I. Introduction

Among many alternative means for knowledge representation, rules seem to be one of the most common forms for expressing various types of knowledge for a number of reasons [1]. It has been argued that other knowledge representation schemes can be transformed into logic (rule)-based schemes [2]–[4]. As such, knowledge-based systems (e.g., rule-based expert systems), usually constructed from human knowledge in forms of if–then rules, have become the most visible and fastest growing branch of artificial intelligence (AI) [1].