Inference-Driven Construction of Valuation Systems from First-Order Clauses

A. Saffiotti and E. Umkehrer

Network-based representations of uncertain knowledge are inherently propositional, and cannot easily accommodate generic, problem independent knowledge. A tremendous effort has been devoted in the knowledge representation community to develop languages that adequately represent different types of generic knowledge. We propose inference-driven construction as a means to use these languages for extending the expressiveness of uncertainty networks: we let a knowledge representation system represent generic knowledge and infer solutions to specific problem instances, and then copy the resulting inference structure to an uncertainty network that models these instances. From a dual perspective, inference-driven construction is a way of extending an existing knowledge representation system by attaching an uncertainty calculus to it. In this paper, we focus on one particular case of inference-driven construction: building Shenoy-Shafer's valuation systems from uncertain knowledge expressed in the form of first order clauses annotated by Dempster-Shafer's measures of belief. We detail an automatic construction procedure for this case, discuss a sample implementation, and provide a soundness and completeness result.
A. Saffiotti and E. Umkehrer Inference-Driven Construction of Valuation Systems from First-Order Clauses. IEEE Transactions on Systems, Man and Cybernetics 24(11):1611-1624, 1994.
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