Decision Support Systems

Meta-interpreters for rule-based inference under uncertainty


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

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prolog, reasoning, rules





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