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Troubleshooting Under Uncertainty

Microsoft Research Technical Report TR-94-07; David Heckerman, John S. Breese, et al.


We develop a series of approximations for decision-theoretic troubleshooting under uncertainty. Our approach generates troubleshooting plans in the face of uncertainty in the relationships among components and device status, observations, as well as the affect of actions on device status. Included in our approach is a Bayesian-network representation of these relationships. We have applied our technique successfully to troubleshooting problems with printing, photocopier feeders, automobiles, and gas turbines. We report empirical findings demonstrating the high quality of plans produced by our approach.

Topics in Decision-Theoretic Troubleshooting: Repair and Experiment

Microsoft Research Technical Report TR-96-06; John S. Breese, David Heckerman.


We develop and extend existing decision-theoretic methods for troubleshooting a non-functioning device. Traditionally, diagnosis with Bayesian networks has focused on belief updating-determining the probabilities of various faults given current observations. In this paper, we extend this paradigm to include taking actions. In particular, we consider three classes of actions: (1) we can make observations regarding the behavior of a device and infer likely faults as in traditional diagnosis, (2) we can repair a component and then observe the behavior of the device to infer likely faults, and (3) we can change the configuration of the device, observe its new behavior, and infer the likelihood of faults. Analysis of latter two classes of troubleshooting actions requires incorporating notions of persistence into the belief-network formalism used for probabilistic inference.

Causal Independence for Probability Assessment and Inference Using Bayesian Networks

Microsoft Research Technical Report TR-94-08; David Heckerman and John S. Breese.


A Bayesian network is a probabilistic representation for uncertain relationships, which has proven to be useful for modeling real-world problems. When there are many potential causes of a given effect, however, both probability assessment and inference using a Bayesian network can be difficult. In this paper, we describe causal independence, a collection of conditional independence assertions and functional relationships that are often appropriate to apply to the representation of the uncertain interactions between causes and effect. We show how the use of causal independence in a Bayesian network can greatly simplify probability assessment as well as probabilistic inference.