Uncertainty in Artificial Intelligence
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Representing and Solving Asymmetric Bayesian Decision Problems
Thomas Nielsen, Finn Jensen
Abstract:
This paper deals with the representation and solution of asymmetric Bayesian decision problems. We present a formal framework, termed asymmetric influence diagrams, that is based on the influence diagram and allows an efficient representation of asymmetric decision problems. As opposed to existing frameworks, the asymmetric influece diagram primarily encodes asymmetry at the qualitative level and it can therefore be read directly from the model. We give an algorithm for solving asymmetric influence diagrams. The algorithm initially decomposes the asymmetric decision problem into a structure of symmetric subproblems organized as a tree. A solution to the decision problem can then be found by propagating from the leaves toward the root using existing evaluation methods to solve the sub-problems.
Keywords:
Pages: 416-425
PS Link:
PDF Link: /papers/00/p416-nielsen.pdf
BibTex:
@INPROCEEDINGS{Nielsen00,
AUTHOR = "Thomas Nielsen and Finn Jensen",
TITLE = "Representing and Solving Asymmetric Bayesian Decision Problems",
BOOKTITLE = "Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-00)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2000",
PAGES = "416--425"
}


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