Meta-Interpreters for Rule-Based Reasoning Under Uncertainty

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One of the key challenges in designing expert systems 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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New York University, Information Systems Department

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