Efficient Test Selection in Active Diagnosis via Entropy Approximation

We address the problem of active diagnosis on a Bayesian network using most-informative test selection. Finding an optimal subset of tests in this setting is intractable in general. We show that it is difficult even to compute the next mostinformative 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 demonstrate promising empirical results on realistic Internet-like topologies.

By: Alice X. Zheng; Irina Rish; Alina Beygelzimer

Published in: RC23663 in 2005


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