Uncertainty in Artificial Intelligence
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Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example
Thomas Nielsen, Pierre-Henri Wuillemin, Finn Jensen, Uffe Kjærulff
Abstract:
When using Bayesian networks for modelling the behavior of man-made machinery, it usually happens that a large part of the model is deterministic. For such Bayesian networks deterministic part of the model can be represented as a Boolean function, and a central part of belief updating reduces to the task of calculating the number of satisfying configurations in a Boolean function. In this paper we explore how advances in the calculation of Boolean functions can be adopted for belief updating, in particular within the context of troubleshooting. We present experimental results indicating a substantial speed-up compared to traditional junction tree propagation.
Keywords:
Pages: 426-435
PS Link:
PDF Link: /papers/00/p426-nielsen.pdf
BibTex:
@INPROCEEDINGS{Nielsen00,
AUTHOR = "Thomas Nielsen and Pierre-Henri Wuillemin and Finn Jensen and Uffe Kjærulff",
TITLE = "Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example",
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 = "426--435"
}


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