User Modeling pp 377-388 | Cite as

Assessing Temporally Variable User Properties With Dynamic Bayesian Networks

  • Ralph Schäfer
  • Thomas Weyrath
Part of the International Centre for Mechanical Sciences book series (CISM, volume 383)


Bayesian networks have been successfully applied to the assessment of user properties which remain unchanged during a session. However, many properties of a person vary over time, thus raising new questions of network modeling. In this paper we characterize different types of dependencies that occur in networks that deal with the modeling of temporally variable user properties. We show how existing techniques of applying dynamic probabilistic networks can be adapted for the task of modeling the dependencies in dynamic Bayesian networks. We illustrate the proposed techniques using examples of emergency calls to the fire department of the city of Saarbrücken. The fire department officers are experienced in dealing with emergency calls from callers whose available working memory capacity is temporarily limited. We develop a model which reconstructs the officers’ assessments of a caller’s working memory capacity.


Bayesian Network User Modeling Time Slice Work Memory Capacity Dynamic Bayesian 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. Calvo, M. G., and Eysenck, M. W. (1996). Phonological working memory and reading in test anxiety. Memory 4:289–305.CrossRefGoogle Scholar
  2. Charniak, E. (1991). Bayesian networks without tears. AI Magazine 12(4):50–63.Google Scholar
  3. Conati, C., and VanLehn, K. (1996). POLA: A student modeling framework for Probabilistic On-Line Assessment of problem solving performance. In Carberry, S., and Zukerman, I., eds., Proceedings of the 5th International Conference on User Modeling. Boston, MA: User Modeling, Inc. 75–82.Google Scholar
  4. Cooper, G. F., Horvitz, E. J., and Heckerman, D. E. (1988). A method for temporal probabilistic reasoning. Technical report, Knowledge Systems Laboratory, Medical Computer Science, Stanford University. Revised April 1989.Google Scholar
  5. Dagum, P., Galper, A., and Horvitz, E. (1992). Dynamic network models for forecasting. In Dubois, D., Wellman, M. P., D’ Ambrosio, B., and Smets, P., eds., Proceedings of the 8th Conference on Uncertainty in Artificial Intelligence, 41–48. San Mateo: Morgan Kaufmann.Google Scholar
  6. Ericsson, K. A., and Simon, H. A. (1993). Protocol Analysis: Verbal Reports as Data. Cambridge, MA: MIT Press, Rev. edition.Google Scholar
  7. Forbes, J., Huang, T., Kanazawa, K., and Russell, S. (1995). The BATmobile: Towards a Bayesian Automated Taxi. In Mellish, C. S., ed., Proceedings of the 14th International Joint Conference on Artificial Intelligence. San Mateo, CA: Morgan Kaufmann. 1878–1885.Google Scholar
  8. Heckerman, D. (1993). Causal independence for knowledge acquisition and inference. In Heckerman, D., and Mamdani, A., eds., Proceedings of the 9th Conference on Uncertainty in Artificial Intelligence, 122–127. Morgan Kaufmann.Google Scholar
  9. Horvitz, E., and Barry, M. (1995). Display of information for time-critical decision making. In Besnard, P., and Hanks, S., eds., Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence. San Francisco: Morgan Kaufmann. 296–305.Google Scholar
  10. Jameson, A. (1996a). Inferenzen über das Arbeitsgedächtnis eines Dialogpartners. In Kluwe, R. H., and May, M., eds., Proceedings der 2. Fachtagung der Gesellschaft für Kognitionswissenschaft. Hamburg: Federal Armed Forces University. 59–61.Google Scholar
  11. Jameson, A. (1996b). Numerical uncertainty management in user and student modeling: An overview of systems and issues. User Modeling and User-Adapted Interaction 5:193–251.CrossRefGoogle Scholar
  12. Jensen, A. L. (1995a). A probabilistic model based support system for mildew management in winter wheat. Ph.D. Dissertation, Aalborg University.Google Scholar
  13. Jensen, A. L. (1995b). Quantification experience of a DSS for mildew management in winter wheat. In Druzdzel, M. J., van der Gaag, L. C., Henrion, M., and Jensen, F. V., eds., IJCAI-95 Workshop on Building Probabilistic Networks: Where do the Numbers Come From, 22–31.Google Scholar
  14. Kashihara, A., Hirashima, T., and Toyoda, J. (1995). A cognitive load application in tutoring. User Modeling and User-Adapted Interaction 4:279–303.CrossRefGoogle Scholar
  15. Kjærulff, U. (1992). A computational scheme for reasoning in dynamic probabilistic networks. In Dubois, D., Wellman, M. P., D’Ambrosio, B., and Smets, P., eds., Proceedings of the 8th Conference on Uncertainty in Artificial Intelligence, 121–129. San Mateo: Morgan Kaufmann.Google Scholar
  16. Neapolitan, R. E. (1990). Probabilistic Reasoning in Expert Systems: Theory and Algorithms. New York: Wiley.Google Scholar
  17. Nicholson, A. E., and Brady, J. M. (1992). The data association problem when monitoring robot vehicles using dynamic belief networks. In Neumann, B., ed., Proceedings of the 10th European Conference on Artificial Intelligence, 689–693.Google Scholar
  18. Nicholson, A. E., and Brady, J. M. (1994). Dynamic belief networks for discrete monitoring. IEEE Transactions on Systems, Man, and Cybernetics 24(11):1593–1610.CrossRefGoogle Scholar
  19. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufmann.Google Scholar
  20. Provan, G. M. (1993). Tradeoffs in constructing and evaluating temporal influence diagrams. In Heckerman, D., and Mamdani, A., eds., Proceedings of the 9th Conference on Uncertainty in Artificial Intelligence, 40–47. San Mateo: Morgan Kaufmann.Google Scholar
  21. Russell, S. J., and Norvig, P. (1995). Artificial Intelligence: A Modern Approach. Englewood Cliffs, NJ: Prentice-Hall.MATHGoogle Scholar
  22. Schum, D. A. (1987). Evidence and Inference for the Intelligence Analyst, volume I. Landam, MD: University Press of America.Google Scholar
  23. Spetzler, C. S., and von Holstein, C.-A. S. S. (1975). Probability encoding in decision analysis. Management Science 22(3):340–358.CrossRefGoogle Scholar
  24. Tawfik, A. Y., and Neufeld, E.(1994). Temporal Bayesian networks. In Goodwin, S. D., and Hamilton, H. J., eds., Proceedings of the TIME-94 — International Workshop on Temporal Representation and Reasoning. Google Scholar
  25. Wahlster, W., Jameson, A., Ndiaye, A., Schäfer, R., and Weis, T. (1995). Ressourcenadaptive Dialogführung: Ein interdisziplinärer Forschungsansatz. Künstliche Intelligenz 9(6):17–21.Google Scholar

Copyright information

© Springer-Verlag Wien 1997

Authors and Affiliations

  • Ralph Schäfer
    • 1
  • Thomas Weyrath
    • 1
  1. 1.Department of Computer ScienceUniversity of SaarbrückenGermany

Personalised recommendations