Bounded conditioning: Flexible inference for decisions under scarce resources.
Horvitz EJ, Suermondt HJ, Cooper GF. Bounded conditioning: Flexible inference for decisions under scarce resources. In: Proceedings of the Workshop on Uncertainty in Artificial Intelligence (1989) 181–193.
We introduce a graceful approach to probabilistic inference called bounded conditioning. Bounded conditioning monotonically re¯nes the bounds on posterior probabilities in a belief network with computation, and converges on ¯nal 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 the expected e®ect that each subproblem will have on the ¯nal 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.
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