Linear Backward Chaining with Knowledge Objects

Abstract

Rule based production systems are one of the most widely used models of knowledge representation in artificial intelligence. However, there are a number of inherent problems with existing rule based systems and tools . Most notably, they are inefficient in structural representation, and rules in general lack of software engineering devices to make them a viable choice for large programs. By applying knowledge object techniques [Wu et al.95] this paper designs a factor-centered representation language, Factor++, which models the rule based paradigm into object-oriented (0-0) programming. Based on Factor++, a linear backward chaining algorithm, LBA, is designed to overcome the large computational requirements in rule based reasoning.