Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment

TL;DR

This paper extends decision-theoretic troubleshooting methods by incorporating actions such as repair and configuration changes into Bayesian network-based diagnosis, enabling more effective fault inference and device repair strategies.

Contribution

It introduces a framework that integrates actions into Bayesian diagnosis, including repair and configuration changes, with considerations for persistence in probabilistic inference.

Findings

Extended Bayesian diagnosis to include actions like repair and configuration changes.

Incorporated persistence into belief-network formalism for troubleshooting.

Enhanced decision-theoretic troubleshooting with action-based inference.

Abstract

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.