Inference-driven construction of valuation systems from first-order clauses

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. The authors propose inference-driven construction as a means to use these languages for extending the expressiveness of uncertainty networks: they 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, the authors 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. The authors detail an automatic construction procedure for this case, discuss a sample implementation, and provide a soundness and completeness result