Uncertainty in Artificial Intelligence
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From influence diagrams to multi-operator cluster DAGs
Cedric Pralet, Thomas Schiex, Gerard Verfaillie
Abstract:
There exist several architectures to solve influence diagrams using local computations, such as the Shenoy-Shafer, the HUGIN, or the Lazy Propagation architectures. They all extend usual variable elimination algorithms thanks to the use of so-called 'potentials'. In this paper, we introduce a new architecture, called the Multi-operator Cluster DAG architecture, which can produce decompositions with an improved constrained induced-width, and therefore induce potentially exponential gains. Its principle is to benefit from the composite nature of influence diagrams, instead of using uniform potentials, in order to better analyze the problem structure.
Keywords:
Pages: 393-400
PS Link:
PDF Link: /papers/06/p393-pralet.pdf
BibTex:
@INPROCEEDINGS{Pralet06,
AUTHOR = "Cedric Pralet and Thomas Schiex and Gerard Verfaillie",
TITLE = "From influence diagrams to multi-operator cluster DAGs",
BOOKTITLE = "Proceedings of the Twenty-Second Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-06)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2006",
PAGES = "393--400"
}


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