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 condi tioning 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. 1 We have pursued the construction of representations and inference strategies that degrade grace fully, and in a well-characterized manner, as the amount of computation applied to reasoning is decreased. In general, the difficulty of a decision problem, the value of solving the problem, and the costs and availability of computation time may vary greatly. We desire inference strategies that are relatively insensitive to small changes in the amount of committed computation time. In particular, strategies that demonstrate ( 1) continuity and (2) monotonicity along a dimension of refinement, and that (3) converge on an optimal result in the limit of sufficient resources, are extremely valu able [7] . Such incremental-refinement approaches are of crucial importance for intelligent agents that depend on computation for decision making under the general condition of varying and un certain resources because they allow for value to be extracted from incomplete solutions [7 ,5]. The strategies provide a continuum of object-level results that grant a system designer or a real-time metareasoner wide ranges over which to optimize the duration of reasoning before acting. The flexible strategies also allow us to squeeze the most inference out of a problem in situations where
Horvitz REF introduced the flexible computing model for time-critical decision making and planning algorithms in Artificial Intelligence.
Bounded Conditioning: Flexible Inference for Decisions under Scarce Resources
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