Uncertainty in Artificial Intelligence
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Evaluating influence diagrams with decision circuits
Debarun Bhattacharjya, Ross Shachter
Abstract:
Although a number of related algorithms have been developed to evaluate influence diagrams, exploiting the conditional independence in the diagram, the exact solution has remained intractable for many important problems. In this paper we introduce decision circuits as a means to exploit the local structure usually found in decision problems and to improve the performance of influence diagram analysis. This work builds on the probabilistic inference algorithms using arithmetic circuits to represent Bayesian belief networks [Darwiche, 2003]. Once compiled, these arithmetic circuits efficiently evaluate probabilistic queries on the belief network, and methods have been developed to exploit both the global and local structure of the network. We show that decision circuits can be constructed in a similar fashion and promise similar benefits.
Keywords:
Pages: 9-16
PS Link:
PDF Link: /papers/07/p9-bhattacharjya.pdf
BibTex:
@INPROCEEDINGS{Bhattacharjya07,
AUTHOR = "Debarun Bhattacharjya and Ross Shachter",
TITLE = "Evaluating influence diagrams with decision circuits",
BOOKTITLE = "Proceedings of the Twenty-Third Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-07)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2007",
PAGES = "9--16"
}


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