User Modeling pp 403-414 | Cite as

Mechanisms for Flexible Representation and Use of Knowledge in User Modeling Shell Systems

  • Wolfgang Pohl
  • Jörg Höhle
Conference paper
Part of the International Centre for Mechanical Sciences book series (CISM, volume 383)


In many user modeling systems, assumptions about the user and other user modeling knowledge are represented in a knowledge base and used for individualized behavior of applications. Hence, user modeling shell systems need to support both representation and use of knowledge. Ideally, a shell provides powerful techniques for applications with sophisticated needs but is also flexible enough to be suitable for applications with specialized, less complex requirements. This paper describes two mechanisms that were developed for increasing the flexibility of user modeling shell systems: the AsTRa (Assumption Type Representation) framework for powerful and flexible logic-based representation of user modeling knowledge; and domain-based user modeling, which allows modularization and sharing of user modeling knowledge bases particularly in centralized user modeling scenarios. Both mechanisms have been implemented in the user modeling shell BGP-MS.


User Modeling View Content Assumption Type Assumption Logic Flexible Representation 
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  1. Brajnik, G., and Tasso, C. (1994). A shell for developing non-monotonic user modeling systems. International Journal of Human-Computer Studies 40:31–62.CrossRefGoogle Scholar
  2. Cohen, P. R. (1978). On knowing what to say: Planning speech acts. Technical Report 118, Department of Computer Science, University of Toronto, Canada.Google Scholar
  3. Finin, T., Fritzson, R., McKay, D., and McEntire, R. (1994). KQML as an agent communication language. In Third International Conference on Knowledge and Information Management, 456–463. New York, NY: ACM Press.Google Scholar
  4. Finin, T. W. (1989). GUMS: A general user modeling shell. In Kobsa, A., and Wahlster, W., eds., User Models in Dialog Systems. Berlin, Heidelberg: Springer. 411–430.CrossRefGoogle Scholar
  5. Fink, J., Kobsa, A., and Nill, A. (1997). Adaptable and adaptive information access for all users, including the disabled and the elderly. In this volume.Google Scholar
  6. Kay, J. (1995). The um toolkit for reusable, long term user models. User Modeling and User-Adapted Interaction 4(3): 149–196.CrossRefMathSciNetGoogle Scholar
  7. Kobsa, A., and Pohl, W. (1995). The user modeling shell system BGP-MS. User Modeling and User-Adapted Interaction 4(2):59–106.CrossRefGoogle Scholar
  8. Kobsa, A. (1985). Benutzermodellierung in Dialogsystemen. Berlin, Heidelberg: Springer-Verlag.CrossRefGoogle Scholar
  9. Kobsa, A. (1992). Towards inferences in BGP-MS: Combining modal logic and partition hierarchies for user modeling. In Proceedings of the Third International Workshop on User Modeling, 35–41.Google Scholar
  10. Orwant, J. (1995). Heterogeneous learning in the Doppelgänger user modeling system. User Modeling and User-Adapted Interaction 4(2): 107–130.CrossRefGoogle Scholar
  11. Paiva, A., and Self, J. (1995). TAGUS—A user and learner modeling workbench. User Modeling and User-Adapted Interaction 4(3): 197–226.CrossRefGoogle Scholar
  12. Pohl, W. (1996). Combining partitions and modal logic for user modeling. In Gabbay, D. M., and Ohlbach, H. J., eds., Practical Reasoning: Proceedings of the International Conference on Formal and Applied Practical Reasoning, 480–494. Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
  13. Pohl, W. (1997). Logic-Based Representation and Inference for User Modeling Shell Systems. Ph.D. Dissertation, University of Essen. Forthcoming.Google Scholar
  14. Rich, E. (1979). User modeling via stereotypes. Cognitive Science 3:329–354.CrossRefGoogle Scholar
  15. Russell, S., and Norvig, P. (1995). Artificial Intelligence: A Modern Approach. Upper Saddle River, NJ: Prentice-Hall.MATHGoogle Scholar
  16. Taylor, J. A., Carletta, J., and Mellish, C. (1996). Requirements for belief models in cooperative dialogue. User Modeling and User-Adapted Interaction 6(1):23–68.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 1997

Authors and Affiliations

  • Wolfgang Pohl
    • 1
  • Jörg Höhle
    • 1
  1. 1.Human-Computer Interaction Research DepartmentGMD FITSt. AugustinGermany

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