Meta-Interpreters for Rule-Based Reasoning Under Uncertainty

One of the key challenges in designing expert systems is a credible representationof uncertainty and partial belief. During the past decade, a number ofrule-based belief languages were proposed and implemented in applied systems.Due to their quasi-probabilistic nature, the external validity of theselanguages is an open question. This paper discusses the theory of belief revisionin expert systems through a canonical belief calculus model which isinvariant across different languages. A meta-interpreter for non-categoricalreasoning is then presented. The purposes of this logic model is twofold:first, it provides a clear and concise conceptualization of belief representationand propagation in rule-based systems. Second, it serves as a workingshell which can be instantiated with different belief calculi. This enablesexperiments to investigate the net impact of alternative belief languages onthe external validity of a fixed expert system