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Strategy Selection in Influence Diagrams using Imprecise Probabilities
This video was recorded at 24th Conference on Uncertainty in Artificial Intelligence (UAI), Helsinki 2008. This paper describes a new algorithm to solve the decision making problem in Influence Diagrams based on algorithms for credal networks. Decision nodes are associated to imprecise probability distributions and a reformulation is introduced that finds the global maximum strategy with respect to the expected utility. We work with Limited Memory Influence Diagrams, which generalize most Influence Diagram proposals and handle simultaneous decisions. Besides the global optimum method, we explore an anytime approximate solution with a guaranteed maximum error and show that imprecise probabilities are handled in a straightforward way. Complexity issues and experiments with random diagrams and an effects-based military planning problem are discussed.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
