The paper concerns the problem of limited range of application of automatically generated mathematic models which are often applied in software agents’ brains. The aim of this paper is to demonstrate that a new approach to the modeling process, integrating knowledge derived from a data set and knowledge derived from a domain expert, originally proposed for modeling economic dependences can be successfully applied in the domain of software agents. Since, the main benefit of this new approach in comparison to other approaches is that it enables application of an automatically generated mathematic models in the whole domain of the underlying relation, it allows the agent to act continuously, without interfering with its user – that means it significantly improves agent’s autonomy.


fuzzy model knowledge integration domain knowledge agent autonomy model applicability 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Shoham, Y.: An Overview of Agent-oriented Programming. In: Bradshaw, J.M. (ed.) Software Agents. AAAI Press, Menlo Park (1997)Google Scholar
  2. 2.
    Jennings, N.R., Wooldridge, M.J.: Agent Technology - Foundations, Applications, and Markets. UNICOM (1998)Google Scholar
  3. 3.
    Lindskog, P.: Fuzzy Identification from a Grey Box Modeling Point of View. In: Hellendoorn, H., Driankov, D. (eds.) Fuzzy Model Identification, pp. 3–50. Springer, Heidelberg (1997)Google Scholar
  4. 4.
    Abonyi, J., Roubos, H., Babuŝka, R., Szeifert, F.: Interpretable Semi-Mechanistic Fuzzy Models by Clustering, OLS and FIS Model Reduction. In: Casillas, J., Cordon, O., Herrera, F., Magdalena, L. (eds.) Modeling and the interpretability-accuracy trade-off. Part I, Interpretability Issues. Studies in Fuzziness and Soft Computing, ch. 10, Physica-Verlag, Heidelberg (2003)Google Scholar
  5. 5.
    Rejer, I.: Training a fuzzy expert model with a set of data points. In: Proceedings on conference on Human System Interaction, Kraków, IEEE Catalog Number: 08EX19995C (2008) ISBN: 1-4244-1543-8Google Scholar
  6. 6.
    O’Leary, D.E.: Knowledge Acquisition from Multiple Experts: An Empirical Study. Management Science 44(8) (August 1998)Google Scholar
  7. 7.
    Wang, C., Hong, T., Tseng, S.: Integrating membership functions and fuzzy rule sets from multiple knowledge sources. Fuzzy Sets and Systems 112 (2000)Google Scholar
  8. 8.
    Rejer, I.: Integration of fuzzy rule bases. Polish Journal of Environmental Studies 16(5B) (2007)Google Scholar
  9. 9.
    Rutkowska, D., Piliński, M., Rutkowski, L.: Neural networks, genetic algorithms and fuzzy systems. Scientific Publishing House Ltd., Warsaw (1999)Google Scholar
  10. 10.
    Chen, M., Linkens, D.A.: Rule-base self-generation and simplification for data-diven fuzzy models. Fuzzy Sets and Systems 142 (2004)Google Scholar
  11. 11.
    Mendel, J.M.: An architecture for making judgements using computing with words. International Journal of Applied Mathematics and Computer Science 12(3), 325–335 (2002)zbMATHMathSciNetGoogle Scholar
  12. 12.
    Rejer, I., Mikołajczyk, M.: A Hypertube as a Possible Interpolation Region of a Neural Model. LNCS (LNAI). Springer, Heidelberg (2006)Google Scholar
  13. 13.
    Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. School of Information and Computer Science. University of California, Irvine (2007), Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Izabela Rejer
    • 1
  1. 1.University of SzczecinSzczecinPoland

Personalised recommendations