A valuation-based language for expert systems

AbstractA new language based on valuations is proposed as an alternative to rule-based languages for constructing knowledge-based systems. Valuation-based languages are superior to rule-based languages for maintaining consistency in the knowledge base, for caching inferences, for managing uncertainty, and for nonmonotonic reasoning. An abstract description of a valuation-based language is given. Two specific instances of valuation-based languages are described. The first is designed to represent categorical knowledge. The ability of such a language to maintain consistency and cache inferences is demonstrated with an example. The second is an evidential language—a valuation-based language in which valuations are belief functions. The ability of evidential languages to perform nonmonotonic reasoning and manage uncertainty is demonstrated with an example