Uncertainty in Artificial Intelligence
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Near-optimal Nonmyopic Value of Information in Graphical Models
Andreas Krause, Carlos Guestrin
Abstract:
A fundamental issue in real-world systems, such as sensor networks, is the selection of observations which most effectively reduce uncertainty. More specifically, we address the long standing problem of nonmyopically selecting the most informative subset of variables in a graphical model. We present the first efficient randomized algorithm providing a constant factor (1-1/e-epsilon) approximation guarantee for any epsilon > 0 with high confidence. The algorithm leverages the theory of submodular functions, in combination with a polynomial bound on sample complexity. We furthermore prove that no polynomial time algorithm can provide a constant factor approximation better than (1 - 1/e) unless P = NP. Finally, we provide extensive evidence of the effectiveness of our method on two complex real-world datasets.
Keywords:
Pages: 324-331
PS Link:
PDF Link: /papers/05/p324-krause.pdf
BibTex:
@INPROCEEDINGS{Krause05,
AUTHOR = "Andreas Krause and Carlos Guestrin",
TITLE = "Near-optimal Nonmyopic Value of Information in Graphical Models",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "324--331"
}


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