The goal of decision-theoretic troubleshooting is to find a sequence of actions that minimizes the expected cost of repair of a device. If the device is complex then it is convenient to create several Bayesian Networks, each designed to solve a particular problem. At the beginning of a troubleshooting process, it is often necessary to help the user to select the proper model. Complications aris...

Technical support contributes 17% of the total cost of ownership of today’s desktop PCs [25]. An important element of technical support is troubleshooting misconfigured applications. Misconfiguration troubleshooting is particularly challenging, because configuration information is shared and altered by multiple applications. In this paper, we present a novel troubleshooting system: PeerPressure...

We consider computer assisted troubleshooting of complex systems, where the objective is to identify the cause of a failure and repair the system at as low expected cost as possible. Three main challenges are: the need for disassembling the system during troubleshooting, the difficulty to verify that the system is fault free, and the dependencies in between components and observations. We prese...

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 t...

We propose a troubleshooting algorithm that can troubleshoot systems with dependent action costs. When actions are performed they may change the way the system is decomposed and affect the cost of future actions. We present a way to model this by extending the traditional troubleshooting model with an additional state that describes which parts of the system that are decomposed. The proposed tr...

An ideal network troubleshooting system would be an almost fully automated system, monitoring the whole network at once, feeding the results to a knowledge-based decision making system that suggests actions to the operator or corrects the failure automatically. Reality is quite the contrary: operators separated in their offices try to track down complex networking failures in their own way, whi...