Printer Troubleshooting Using Bayesian Networks
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  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.
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