Combination of Bayesian Network and Overlay Model in User Modeling

  • Loc Nguyen
  • Phung Do
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5545)


The core of adaptive system is user model containing personal information such as knowledge, learning styles, goals which is requisite for learning personalized process. There are many modeling approaches, for example: stereotype, overlay, plan recognition... but they do not bring out the solid method for reasoning from user model. This paper introduces the statistical method that combines Bayesian network and overlay modeling so that it is able to infer user’s knowledge from evidence collected during user’s learning process.


Bayesian network overlay model user model 


  1. 1.
    Akiba, T., Tanaka, H.: A Bayesian Approach for User Modeling in Dialogue Systems. In: COLING 1994. The 15th International Conference on Computational Linguistics, vol. 1 (1994)Google Scholar
  2. 2.
    Allen, R.B.: User Models: Theory, Method, and Practice. International Journal of Man-Machine Studies 32, 511–543 (1990)CrossRefGoogle Scholar
  3. 3.
    Brachman, R.J., Levesque, H.J.: Knowledge Representation and Reasoning. ©2003 CMPT 411/882 Course Home Page (Spring 2005)Google Scholar
  4. 4.
    Bunt, A., Conati, C.: Probabilistic Student Modelling to Improve Exploratory Behaviour. Journal of User Modeling and User-Adapted Interaction 13(3), 269–309 (2003)CrossRefGoogle Scholar
  5. 5.
    Conati, C.: Probabilistic Assessment of User’s Emotions in Educational Games. Journal of Applied Artificial Intelligence, special issue on “ Merging Cognition and Affect in HCI” 16(7-8), 555–575 (2002)CrossRefGoogle Scholar
  6. 6.
    Charniak, E.: Bayesian Network without Tears. AI magazine (1991)Google Scholar
  7. 7.
    Chin, D.N.: A Case Study of Knowledge Representation in UC. In: Proceedings of the Eighth International Joint Conference on Artificial Intelligence, August 1983, vol. 1, pp. 388–390. Karlsruhe, West Germany (1983)Google Scholar
  8. 8.
    Chin, D.N.: KNOME: Modeling What the User Knows in UC. In: Kobsa, A., Wahlster, W. (eds.) User Models in Dialog Systems, pp. 74–107. Springer, HeidelbergGoogle Scholar
  9. 9.
    Fagin, R., Halpern, J.Y.: Reasoning about knowledge and probability. ACM 41(2), 340–367 (1994); Preliminary version appeared in: Vardi, M.Y. (ed.): Second Conf. on Theoretical Aspects of Reasoning about Knowledge, pp. 277-293. Morgan Kaufmann (1988); Corrigendum: J. ACM 45(1), p. 214 (January 1998)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Finin, T., Drager, D.: GUMS: A General User Modeling System. In: Proceedings of the Canadian Society for Computational Studies of Intelligence 1986 (CSCSI 1986) (1986)Google Scholar
  11. 11.
    Halpern, J.Y.: Reasoning about Knowledge,
  12. 12.
    Halpern, J.Y.: Reasoning about Uncertainty,
  13. 13.
    Henze, N., Nejdl, W.: Bayesian Modeling for Adaptive Hypermedia Systems. Knowledge Based Systems Group, University of Hannover, Lange Laube 3, 30159 Hannover, Germany (1999)Google Scholar
  14. 14.
    Horvitz, E., Breese, J., Heckerman, D., Hovel, D., Rommelse, K.: The Lumière Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, July 1998, pp. 256–265. Morgan Kaufmann, San Francisco (1998)Google Scholar
  15. 15.
    Jameson, A.: Logic is not enough: Why reasoning about another person’s beliefs is reasoning under uncertainty. In: Laux, A., Wansing, H. (eds.) Knowledge and Belief in Philosophy and Artificial Intelligence. Akademie Verlag, Berlin (1995)Google Scholar
  16. 16.
    Jameson, A., Hoeppner, W., Wahlster, W.: The Natural Language System HAM-RPM as a Hotel Manager: Some Representational Prerequisites. In: Wilhelm, R. (ed.) GI - 10. Jahrestagung Saarbrücken. Springer, Berlin (1980)Google Scholar
  17. 17.
    Jedlitschka, A., Althoff, K.-D.: Using Case-Based Reasoning for User Modeling in an Experience Management System. In: Proc. Workshop Adaptivität und Benutzermodellierung in Interaktiven Systemen (ABIS 2001), GI-Workshop-Woche Lernen - Lehren - Wissen - Adaptivität (LLWA 2001), Universität Dortmund, October 8-12 (2001)Google Scholar
  18. 18.
    Johansson, P.: User Modeling in Dialog Systems. St. Anna Report: SAR 02-2Google Scholar
  19. 19.
    Kobsa, A.: First experiences with the SB-ONE knowledge representation workbench in natural-language applications. ACM SIGART Bulletin 2(3), 70–76 (1991)CrossRefGoogle Scholar
  20. 20.
    Kobsa, A.: User Modeling: Recent Work, Prospects and Hazards. Department of Computer Science, Columbia University, New York, USA (1993)Google Scholar
  21. 21.
    Orwant, J.L.: Doppelgänger Goes To School: Machine Learning for User Modeling. Master’s thesis, Massachusetts Institute of Technology (1993)Google Scholar
  22. 22.
    Paiva, A., Self, J.: A Learner Model Reason Maintenance System. In: Cohn, A. (ed.) ECAI 1994. 11th European Conference on Artificial Intelligence. John Wiley & Sons, Ltd., Chichester (1994)Google Scholar
  23. 23.
    Papatheodorou, C.: Machine Learning in User Modeling. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds.) ACAI 1999. LNCS (LNAI), vol. 2049, pp. 286–294. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  24. 24.
    Pohl, W.: Logic-Based Representation and Reasoning for User Modeling Shell Systems. In: User Modeling and User-Adapted Interaction (UMUAI 1999), vol. 9(3), pp. 217–283 (1999)Google Scholar
  25. 25.
    Pohl, W., Höhle, J.: Mechanisms for flexible representation and use of knowledge in user modeling shell systems. In: Jameson, A., Paris, C., Tasso, C. (eds.) User Modeling: Proceedings of the Sixth International Conference, UM 1997, CISM 1997. Springer, Vienna (1997)Google Scholar
  26. 26.
    Poole, D.: A Logical Framework for Default Reasoning. Logic Programming and Artificial Intelligence Group, Department of Computer Science, University of Waterloo, Waterloo, Ontario, Canada, N2L3G1 (519), 888–4443 (1987)Google Scholar
  27. 27.
    Rich, E.: User Modeling via Stereotypes. Cognitive Science 3, 329–354 (1979)CrossRefGoogle Scholar
  28. 28.
    Sparacino, F.: Sto(ry)chastics: a Bayesian Network Architecture for User Modeling and Computational Storytelling for Interactive Spaces. LNCS. Springer, Heidelberg (2003)Google Scholar
  29. 29.
    Ting, C.Y., Phon-Amnuaisuk, S., Chong, Y.K.: Modeling and Intervening Across Time in Scientific Inquiry Exploratory Learning Environment. Journal of Educational Technology & Society 11(3), 239–258 (2008)Google Scholar
  30. 30.
    Ting, C.Y., Reza Beik Zadeh, M.: Assessing Learner’s Scientific Inquiry Skills Across Time: A Dynamic Bayesian Network Approach. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS, vol. 4511, pp. 207–216. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  31. 31.
    Tedesco, R., Dolog, P., Nejdl, W., Allert, H.: Distributed Bayesian Networks for User Modeling. In: ELEARN 2006: World Conference on E-Learning in Corporate, Government, Health Care, and Higher Education (2006)Google Scholar
  32. 32.
    wikipedia. Default Logic (2007),
  33. 33.
    Wilensky, R.: Some Problems and Proposals for Knowledge Representation. Computer Science Division, University of California, Berkely, Report No. UCB/CSD 87/351 (1987)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Loc Nguyen
    • 1
  • Phung Do
    • 2
  1. 1.Faculty of Information TechnologyUniversity of Natural ScienceHo Chi Minh CityVietnam
  2. 2.Faculty of Information SystemUniversity of Information TechnologyHo Chi Minh CityVietnam

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