Information-Theoretic Approaches to Cost-Efficient Diagnosis

This paper provides a summary of our recent work on cost-efficient probabilistic diagnosis in Bayesian networks with applications to fault diagnosis in distributed computer systems. We focus on achieving good trade-offs between the diagnostic accuracy versus the cost of testing and computational complexity of diagnosis. We present (1) theoretical results characterizing these trade-offs, such as lower bound on the number of probes necessary to achieve asymptotically error-free diagnosis, (2) adaptive online approach to selecting most-informative tests, as well as (3) approximation techniques using ”loopy” belief propagation for handling intractable inference problems involved in both diagnosis and most-informative test selection in large-scale problems. Empirical results on realistic systems demonstrating the effectiveness of our approaches can be found in [9], [16], [13], [15].

By: Irina Rish

Published in: RC24414 in 2007


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