Abstract
Bayesian Networks (BNs) have been extensively used for diagnosis applications. Knowledge acquisition (KA), i.e. building a BN from the knowledge of experts in the application domain, involves two phases: knowledge gathering and model construction, i.e. defining the model based on that knowledge. The number of parameters involved in a large network is normally intractable to be specified by human experts. This leads to a trade-off between the accuracy of a detailed model and the size and complexity of such a model. In this paper, a Knowledge Acquisition Tool (KAT) to automatically perform information gathering and model construction for diagnosis of the radio access part of cellular networks is presented. KAT automatically builds a diagnosis model based on the experts’ answers to a sequence of questions regarding his way of reasoning in diagnosis. This will be performed for two BN structures: Simple Bayes Model (SBM) and Independence of Causal Influence (ICI) models.
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Barco, R., Lázaro, P., Wille, V., Díez, L. (2006). Knowledge Acquisition for Diagnosis in Cellular Networks Based on Bayesian Networks. In: Lang, J., Lin, F., Wang, J. (eds) Knowledge Science, Engineering and Management. KSEM 2006. Lecture Notes in Computer Science(), vol 4092. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811220_6
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DOI: https://doi.org/10.1007/11811220_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37033-8
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