Knowledge Acquisition for Diagnosis in Cellular Networks Based on Bayesian Networks

  • Raquel Barco
  • Pedro Lázaro
  • Volker Wille
  • Luis Díez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4092)


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.


Bayesian Network Cellular Network Knowledge Acquisition Model Construction Radio Access Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Raquel Barco
    • 1
  • Pedro Lázaro
    • 1
  • Volker Wille
    • 2
  • Luis Díez
    • 1
  1. 1.Departamento de Ingeniería de ComunicacionesUniversidad de MálagaMálagaSpain
  2. 2.Nokia Networks, Performance ServicesHuntingdon, CambridgeUK

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