Abstract
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.
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Skaanning, C., Jensen, F.V., Kjærulff, U. (2000). Printer Troubleshooting Using Bayesian Networks. In: Logananthara, R., Palm, G., Ali, M. (eds) Intelligent Problem Solving. Methodologies and Approaches. IEA/AIE 2000. Lecture Notes in Computer Science(), vol 1821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45049-1_45
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DOI: https://doi.org/10.1007/3-540-45049-1_45
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