The use of design descriptions in automated diagnosis
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
Probabilistic evaluation of counterfactual queries
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Decision-theoretic troubleshooting
Communications of the ACM
Decision-theoretic foundations for causal reasoning
Journal of Artificial Intelligence Research
Causal independence for probability assessment and inference using Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Probabilities of causation: Bounds and identification
Annals of Mathematics and Artificial Intelligence
Sequential Decision Models for Expert System Optimization
IEEE Transactions on Knowledge and Data Engineering
Monitoring and diagnosis of a multistage manufacturing process using Bayesian networks
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Designing for Diagnosing: Introduction to the Special Issue on Diagnostic Work
Computer Supported Cooperative Work
Troubleshooting when Action Costs are Dependent with Application to a Truck Engine
Proceedings of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008
Extensions of decision-theoretic troubleshooting: cost clusters and precedence constraints
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Using ROBDDs for inference in Bayesian networks with troubleshooting as an example
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Probabilities of causation: bounds and identification
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Computing the optimal action sequence by niche genetic algorithm
KI'05 Proceedings of the 28th annual German conference on Advances in Artificial Intelligence
Engineering Applications of Artificial Intelligence
Decision-theoretic troubleshooting: Hardness of approximation
International Journal of Approximate Reasoning
| Hi-index | 0.00 |
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.