Skip to main content
Springer Nature Link
Log in

Decision Making in a Dynamic System Based on Aggregated Fuzzy Preferences

  • Conference paper
Modeling Decisions for Artificial Intelligence (MDAI 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3131))

  • 804 Accesses

Abstract

The fuzzy preference is related to decision making in artificial intelligence. A mathematical model for dynamic and stochastic decision making together with perception and cognition is presented. This paper models human behavior based on the aggregated fuzzy preferences, and an objective function induced from the fuzzy preferences is formulated. In dynamic decision making, there exists a difficulty when we formulate the objective function from fuzzy preferences since the value criterion of fuzzy preferences in dynamic behavior transforms together with time and it is formulated gradually based on the experience. A reasonable criterion based on fuzzy preferences is formulated for the dynamic decision making, and an optimality equation for this model is derived by dynamic programming. Mathematical models to simulate human behavior with his decision making are applicable to various fields: robotics, customers’ behavior analysis in marketing, multi-agent systems and so on.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Coubon, J.-Cl., Dubois, D., Roy, B.: Autour de l’aide à la décision et de l’intelligence artificielle. Rapport LAFORIA/IBP 94/01 (1994)

    Google Scholar 

  2. Fishburn, P.C.: Utility Theory for Decision Making. John Wiley and Sons, New York (1970)

    MATH  Google Scholar 

  3. Fodor, J., Roubens, M.: Fuzzy Preference Modelling and Multi-Criteria Decision Support. Kluwer Academic Publishers, Dordrecht (1994)

    Google Scholar 

  4. Ishibuchi, H., Tanaka, H.: Fuzzy regression analysis using neural networks. Fuzzy Sets and Systems 50, 257–265 (1992)

    Article  MathSciNet  Google Scholar 

  5. Kaminka, G.A., Lima, P.U., Rojas, R. (eds.): RoboCup 2002. LNCS (LNAI), vol. 2752. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  6. Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice-Hall, London (1995)

    MATH  Google Scholar 

  7. Kurano, M., Yasuda, M., Nakagami, J., Yoshida, Y.: Markov decision processes with fuzzy rewards. J. Nonlinear Convex Analysis 4, 105–115 (2003)

    MATH  MathSciNet  Google Scholar 

  8. Kwakernaak, H.: Fuzzy random variables – I. Definitions and theorem. Inform. Sci. 15, 1–29 (1978)

    MATH  MathSciNet  Google Scholar 

  9. von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press, Princeton (1944)

    MATH  Google Scholar 

  10. Newell, A., Simon, H.A.: Human Problems Solving. Englewood. Prentice-Hall, Englewood Cliffs (1972)

    Google Scholar 

  11. Pomerol, J.-C.: Artificial intelligence and human decision making. European Journal of Operational Research 99, 3–25 (1997)

    Article  MATH  Google Scholar 

  12. Puri, M.L., Ralescu, D.: The concept of normality for fuzzy random variables. Ann. Prob. 13, 1373–1379 (1985)

    Article  MATH  MathSciNet  Google Scholar 

  13. Simon, H.A.: The New Science of Management Decision. Prentice-Hall, Englewood Cliffs (1963)

    Google Scholar 

  14. Simon, H.: The Sciences of the Artificial. MIT Press, Cambridge (1969)

    Google Scholar 

  15. Slotine, J.J.E., Li, W.: Applied Nonlinear Control. Prentice-Hall, Englewood Cliffs (1991)

    MATH  Google Scholar 

  16. Yoshida, Y.: Continuous-time fuzzy decision processes with discounted rewards. Fuzzy Sets and Systems 139, 33–348 (2003)

    Google Scholar 

  17. Zadeh, L.A.: Fuzzy sets. Inform. and Control 8, 338–353 (1965)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Economics and Business Administration, University of Kitakyushu, 4-2-1 Kitagata, Kokuraminami, Kitakyushu, 802-8577, Japan

    Yuji Yoshida

Authors
  1. Yuji Yoshida
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. IIIA, Artificial Intelligence Research Institute CSIC, Spanish National Research Council, Campus UAB s/n, 08193, Bellaterra, Catalonia, Spain

    Vicenç Torra

  2. Toho Gakuen, 3-1-10 Naka, Kunitachi, 186-0004, Tokyo, Japan

    Yasuo Narukawa

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yoshida, Y. (2004). Decision Making in a Dynamic System Based on Aggregated Fuzzy Preferences. In: Torra, V., Narukawa, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2004. Lecture Notes in Computer Science(), vol 3131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27774-3_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-27774-3_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22555-3

  • Online ISBN: 978-3-540-27774-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics