Uncertainty in Artificial Intelligence
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Efficient Test Selection in Active Diagnosis via Entropy Approximation
Alice Zheng, Irina Rish, Alina Beygelzimer
We consider the problem of diagnosing faults in a system represented by a Bayesian network, where diagnosis corresponds to recovering the most likely state of unobserved nodes given the outcomes of tests (observed nodes). Finding an optimal subset of tests in this setting is intractable in general. We show that it is difficult even to compute the next most-informative test using greedy test selection, as it involves several entropy terms whose exact computation is intractable. We propose an approximate approach that utilizes the loopy belief propagation infrastructure to simultaneously compute approximations of marginal and conditional entropies on multiple subsets of nodes. We apply our method to fault diagnosis in computer networks, and show the algorithm to be very effective on realistic Internet-like topologies. We also provide theoretical justification for the greedy test selection approach, along with some performance guarantees.
Pages: 675-682
PS Link:
PDF Link: /papers/05/p675-zheng.pdf
AUTHOR = "Alice Zheng and Irina Rish and Alina Beygelzimer",
TITLE = "Efficient Test Selection in Active Diagnosis via Entropy Approximation",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "675--682"

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