Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment
This work addresses device repair challenges for technicians, but it is incremental as it builds on existing decision-theoretic methods.
The paper tackles the problem of troubleshooting nonfunctioning devices by extending Bayesian network methods to include actions like repair and configuration changes, resulting in a framework that incorporates persistence for probabilistic inference.
We develop and extend existing decision-theoretic methods for troubleshooting a nonfunctioning 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.