The use of design descriptions in automated diagnosis
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
Decision-theoretic troubleshooting
Communications of the ACM
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
HUGIN: a shell for building Bayesian belief universes for expert systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Why is diagnosis using belief networks insensitive to imprecision in probabilities?
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
WISECON - An Intelligent Assistant for Buying Computers on the Internet
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
On stopping evidence gathering for diagnostic Bayesian networks
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
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
A knowledge acquisition tool for Bayesian-network troubleshooters
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
Systematic causal knowledge acquisition using FCM Constructor for product design decision support
Expert Systems with Applications: An International Journal
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This paper describes a real world Bayesian network application - diagnosis of a printing system. The diagnostic problem is represented in a simple Bayes model which is sufficient under the single-fault assumption. The construction of this Bayesian network structure is described, along with guidelines for acquiring the necessary knowledge. Several extensions to the algorithms of [2] for finding the best next step are presented. The troubleshooters are executed with custom-built troubleshooting software that guides the user through a good sequence of steps. Screenshots from this software is shown.