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 [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.