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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Andreassen, S., Woldbye, M., Falck, B., Andersen, S.: MUNIN: A causal probabilistic network for interpretation of electromyographic findings. In: Proc. International Joint Conference on Artificial Intelligence, Milan, Italy, August 1987, pp. 366–372 (1987)Google Scholar
  2. 2.
    Barco, R.: Knowledge acquisition tool specification. Nokia Networks, Málaga, Spain, Tech. Rep. AutoGERAN_KAT_2001_1H_v1_0 (June 2001)Google Scholar
  3. 3.
    Barco, R., Guerrero, R., Hylander, G., Nielsen, L., Partanen, M., Patel, S.: Automated troubleshooting of mobile networks using bayesian networks. In: Proc. IASTED Int. Conf. Communication Systems and Networks (CSN 2002), Málaga, Spain, September 2002, pp. 105–110 (2002)Google Scholar
  4. 4.
    Barco, R., Wille, V., Díez, L., Lázaro, P.: Comparison of probabilistic models used for diagnosis in cellular networks. In: Proc. Vehicular Technology Conference (VTC), Melbourne, Australia (May 2006)Google Scholar
  5. 5.
    Blake, C., Merz, C.: UCI repository of machine learning databases. Dept. Information and Computer Science. University of California, Irvine, (Online) Available:
  6. 6.
    Buntine, W.: A guide to the literature on learning graphical models. IEEE Trans. Knowledge Data Eng. 8, 195–210 (1996)CrossRefGoogle Scholar
  7. 7.
    Druzdzel, M.J., van der Gaag, L.C.: Elicitation of probabilities for belief networks: combining qualitative and quantitative information. In: Proc. Annual Conf. Uncertainty in Artificial Intelligence, Montreal, Canada, August 1995, pp. 141–148 (1995)Google Scholar
  8. 8.
    Druzdzel, M.J., van der Gaag, L.C.: Building probabilistic networks: where do the numbers come from? IEEE Trans. Knowledge Data Eng. 12(4), 481–486 (2000)CrossRefGoogle Scholar
  9. 9.
    van der Gaag, L., Renooij, S., Witteman, C., Aleman, B., Taal, B.: How to elicit many probabilities. In: Proc. Annual Conf. Uncertainty in Artificial Intelligence, Stockholm, Sweden, pp. 647–654 (July 1999)Google Scholar
  10. 10.
    Heckerman, D., Breese, J.: Causal independence for probability assessment and inference using bayesian networks. Microsoft Research, Redmond, Washington, Tech. Rep. MSR-TR-94-08 (March 1994)Google Scholar
  11. 11.
    Heckerman, D., Breese, J.: A new look at causal independence. In: Proc. Annual Conf. Uncertainty in Artificial Intelligence, Seattle, Washington, pp. 286–292 (July 1994)Google Scholar
  12. 12.
    Heckerman, D., Breese, J., Rommelse, K.: Decision-theoretic troubleshooting. Communication of the ACM 38(3), 49–57 (1995)CrossRefGoogle Scholar
  13. 13.
    Heckerman, D.: A tutorial on learning bayesian networks. Microsoft Research, Redmond, Washington, Tech. Rep. MSR-TR-95-06 (March 1995)Google Scholar
  14. 14.
    Henrion, M.: Some practical issues in constructing belief networks. In: Kanal, L., Leuitt, T., Lemmer, J. (eds.) Uncertainty in Artificial Intelligence, vol. 3, pp. 161–173. Elsevier Science, Amsterdam (1989)Google Scholar
  15. 15.
    Jensen, F.: Bayesian Networks and decision graphs. Springer, New York (2001)MATHGoogle Scholar
  16. 16.
    Neapolitan, R.: Learning Bayesian Networks. Prentice-Hall, Englewood Cliffs (2004)Google Scholar
  17. 17.
    Pearl, J.: Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann, San Francisco (1988)Google Scholar
  18. 18.
    Renooij, S., Witteman, C.: Talking probabilities: communicating probabilistic information with words and numbers. International Journal of Approximate Reasoning 22(3), 169–194 (1999)CrossRefGoogle Scholar
  19. 19.
    Salton, G., Allen, J., Buckley, C.: Automatic structuring and retrieval of large text files. Communications of the ACM 37(2), 97–108 (1994)CrossRefGoogle Scholar
  20. 20.
    Skaanning, C., Jensen, F., Kjærulff, U., Madsen, A.: Acquisition and transformation of likelihoods to conditional probabilities for bayesian networks. In: Proc. AAAI Spring Symposium on AI in Equipment Maintenance Service and Support, Palo Alto, California, March 1999, pp. 34–40 (1999)Google Scholar
  21. 21.
    Skaanning, C.: A knowledge acquisition tool for bayesian-network troubleshooters. In: Proc. Annual Conf. Uncertainty in Artificial Intelligence, Stanford, USA, July 2000, pp. 549–557 (2000)Google Scholar
  22. 22.
    Srinivas, S.: A generalization of the noisy-or model. In: Proc. Annual Conf. Uncertainty in Artificial Intelligence, Washington, USA, July 1993, pp. 208–215 (1993)Google Scholar
  23. 23.
    Steinder, M., Sethi, A.: Probabilistic fault localization in communication systems using belief networks. IEEE/ACM Trans. Networking 12(5), 809–822 (2004)CrossRefGoogle Scholar

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

Personalised recommendations