Operations Research
Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Flexible policy construction by information refinement
Flexible policy construction by information refinement
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Anytime synthetic projection: maximizing the probability of goal satisfaction
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
An anytime algorithm for decision making under uncertainty
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Using performance profile trees to improve deliberation control
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Modeling challenges with influence diagrams: Constructing probability and utility models
Decision Support Systems
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We outline a method to estimate the value of computation for a flexible algorithm using empirical data. To determine a reasonable trade-off between cost and value, we build an empirical model of the value obtained through computation, and apply this model to estimate the value of computation for quite different problems. In particular, we investigate this trade-off for the problem of constructing policies for decision problems represented as influence diagrams. We show how two features of our anytime algorithm provide reasonable estimates of the value of computation in this domain.