Bounded Conditioning: Flexible Inference for Decisions Under Scarce Resources

Eric Horvitz, H. Jacques Suermondt, Gregory F. Cooper

Medical Computer Science Group
Knowledge Systems Laboratory
Stanford University
Stanford, California 94305

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We introduce a graceful approach to probabilistic inference called Bounded Conditioning. Bounded Conditioning monotonically refines the bounds on posterior probabilities in a belief network with computation, and converges on final probabilities of interest with the allocation of a complete resource fraction. The approach allows a reasoner to exchange arbitrary quantities of computational resource for incremental gains in inference quality. As such, Bounded Conditioning holds promise as a useful inference technique for reasoning under the general conditions of uncertain and varying reasoning resources. The algorithm solves a probabilistic bounding problem in complex belief networks by breaking the problem into a set of mutually exclusive, tractable subproblems and ordering their solution by theexpected effect that each subproblem will have on the final answer. We introduce the algorithm, discuss its characterization, and present its performance on several belief networks, including a complex model for reasoning about problems in intensive-care medicine.

Keywords: Decision making under scarce resources, planning under bounded resources, Bayesian reasoning, probabilistic inference algorithms, flexible computation.

E. Horvitz, H.J. Suermondt, G.F. Cooper. Bounded Conditioning: Flexible inference for decisions under scarce resources. In: Proceedings of Conference on Uncertainty in Artificial Intelligence, Windsor, ON. August 1989. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, pp. 182-193.