Operations Research
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
Flexible policy construction by information refinement
Flexible policy construction by information refinement
Machine Learning
Input generalization in delayed reinforcement learning: an algorithm and performance comparisons
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Flexible policy construction by information refinement
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Knowledge Management to Support Situation-aware Risk Management in Autonomous, Self-managing Agents
Proceedings of the 2005 conference on Self-Organization and Autonomic Informatics (I)
A forward-backward Monte Carlo method for solving influence diagrams
International Journal of Approximate Reasoning
Modeling challenges with influence diagrams: Constructing probability and utility models
Decision Support Systems
Probabilistic graphical models in artificial intelligence
Applied Soft Computing
Estimating the value of computation in flexible information refinement
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Bayesian control for concentrating mixed nuclear waste
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Besting the quiz master: crowdsourcing incremental classification games
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Anytime algorithms for top-N recommenders
Proceedings of the 7th ACM conference on Recommender systems
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We present an anytime algorithm which computes policies for decision problems represented as multi-stage influence diagrams. Our algorithm constructs policies incrementally, starting from a policy which makes no use of the available information. The incremental process constructs policies which includes more of the information available to the decision maker at each step. While the process converges to the optimal policy, our approach is designed for situations in which computing the optimal policy is infeasible. We provide examples of the process on several large decision problems, showing that, for these examples, the process constructs valuable (but sub-optimal) policies before the optimal policy would be available by traditional methods.