Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Operations Research
Valuation-based systems for Bayesian decision analysis
Operations Research
Decision making using probabilistic inference methods
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Welldefined decision scenarios
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Efficient value of information computation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Probabilistic inference in influence diagrams
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Probabilistic graphical models in artificial intelligence
Applied Soft Computing
Exploiting model equivalences for solving interactive dynamic influence diagrams
Journal of Artificial Intelligence Research
International Journal of Approximate Reasoning
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We present a new approach to the solution of decision problems formulated as influence diagrams. The approach converts the influence diagram into a simpler structure, the LImited Memory Influence Diagram (LIMID), where only the requisite information for the computation of optimal policies is depicted. Because the requisite information is explicitly represented in the diagram, the evaluation procedure can take advantage of it. In this paper we show how to convert an influence diagram to a LIMID and describe the procedure for finding an optimal strategy. Our approach can yield significant savings of memory and computational time when compared to traditional methods.