Bounded Conditioning: Flexible Inference for Decisions Under Scarce Resources

Reference: Horvitz, E. J.; Suermondt, H. J.; & Cooper, G. F. Bounded Conditioning: Flexible Inference for Decisions Under Scarce Resources. Association for Uncertainty in Artificial Intelligence, WIndsor, ON, 1989.

Abstract: We introduce an incremental-refinement approach to probabilistic inference called bounded conditioning. Bounded conditioning monotonically refines the bounds on probabilities in a belief network with computation, and converges on a final probability of interest with the allocation of a complete resource fraction. As such, the approach holds promise as a useful inference technique for reasoning under the general conditions of uncertain and varying reasoning resources. The algorithm can solve a great portion of a probabilistic bounding problem in complex belief networks through breaking the world into a set of mutually exclusive, tractable subproblems and ordering their solution by the expected effect that each subproblem will have on the final answer. We introduce the algorithm, discuss its worst-case characterization, and present its performance on a complex belief network for reasoning about problems in the intensive-care unit.

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