Uncertainty in Artificial Intelligence
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Bounded Conditioning: Flexible Inference for Decisions under Scarce Resources
Eric Horvitz, Jaap Suermondt, Gregory Cooper
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 the expected 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.
Pages: 182-193
PS Link:
PDF Link: /papers/89/p182-horvitz.pdf
AUTHOR = "Eric Horvitz and Jaap Suermondt and Gregory Cooper",
TITLE = "Bounded Conditioning: Flexible Inference for Decisions under Scarce Resources",
BOOKTITLE = "Proceedings of the Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-89)",
ADDRESS = "Corvallis, Oregon",
YEAR = "1989",
PAGES = "182--193"

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