# 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

** Author Email: ** ` horvitz@microsoft.com`

** Click here to access pdf file.**

### Abstract:

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.