Nonmonotonic Reasoning and Computational Knowledge Representation

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IIS-0097278Miroslaw Truszczynski, Raphael A. Finkel and Victor MarekUniversity of Kentucky$146,167 - 12 mosNonmonotonic Reasoning and Computational Knowledge RepresentationThis is the first year funding of a three year continuing award. The PI will study and implement computational knowledge-representation systems based on the paradigm of answer-set programming (ASP) with nonmonotonic logic that he recently identified, in order to demonstrate the practicality and effectiveness of the approach. Logic is most commonly used in knowledge representation as follows. To solve a problem we represent its constraints and the relevant background knowledge as a theory in the language of first-order logic (or its fragment). We formulate the goal (the statement of the problem) as a formula of the logic. We then use proof techniques to decide whether this formula follows from the theory. A proof of the formula, variable substitutions or both determine a solution. Taking a different approach, the PI will study and develop computational knowledge representation tools based on nonmonotonic logics rather than on the first-order logic. In addition, he departs from the single-intended model approach dominant in logic programming. Under the ASP paradigm, a theory in a nonmonotonic formalism is regarded as a specification of a family of sets - a collection of its intended models. Each model is viewed as a representation of a different single solution. The PI will investigate syntactic and semantic issues of ASP formalisms based on nonmonotonic logics, study methods for fast computing with these formalisms, develop practical implementations, and demonstrate effectiveness of answer-set programming engines and their applicability in knowledge representation. If successful, the work will establish answer-set programming as a viable approach to declarative programming, which in turn will provide AI researchers and practitioners with a new generation of computational tools for knowledge representation.