Incremental Tradeoff Resolution in Qualitative Probabilistic Networks

ions, Decisions, and Uncertainty, Providence, RI, USA, July 1997 Incremental Tradeoff Resolution in Qualitative Probabilistic Networks Chao-Lin Liu and Michael P. Wellman University of Michigan AI Laboratory Ann Arbor, MI 48109-2110 fchaolin, wellmang@umich.edu Abstract Qualitative probabilistic reasoning in a Bayesian network often reveals tradeoffs: relationships that are ambiguous due to competing qualitative influences. We present two techniques that combine qualitative and numeric probabilistic reasoning to resolve such tradeoffs, inferring the qualitative relationship between nodes in a Bayesian network. The first approach incrementally marginalizes nodes in network, and the second incrementally refines the state spaces of random variables. Both provide systematic methods for tradeoff resolution at potentially lower computational cost than application of purely numeric methods. Introduction Researchers in uncertain reasoning regularly observe that to reach a desired conclu..