Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Decision making using probabilistic inference methods
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Structuring conditional relationships in influence diagrams
Operations Research
Machine Learning
Modeling challenges with influence diagrams: Constructing probability and utility models
Decision Support Systems
An anytime algorithm for decision making under uncertainty
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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We report on work towards flexible algorithms for solving decision problems represented as influence diagrams. An algorithm is given to construct a tree structure for each decision node in an influence diagram. Each tree represents a decision function and is constructed incrementally. The improvements to the tree converge to the optimal decision function (neglecting computational costs) and the asymptotic behaviour is only a constant factor worse than dynamic programming techniques, counting the number of Bayesian network queries. Empirical results show how expected utility increases with the size of the tree and the number of Bayesian net calculations.