Uncertainty in Artificial Intelligence
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Strategy Selection in Influence Diagrams using Imprecise Probabilities
Cassio de Campos, Qiang Ji
Abstract:
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.
Keywords:
Pages: 121-128
PS Link:
PDF Link: /papers/08/p121-de_campos.pdf
BibTex:
@INPROCEEDINGS{de Campos08,
AUTHOR = "Cassio de Campos and Qiang Ji",
TITLE = "Strategy Selection in Influence Diagrams using Imprecise Probabilities",
BOOKTITLE = "Proceedings of the Twenty-Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-08)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2008",
PAGES = "121--128"
}


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