Human Behavioral Modeling Using Fuzzy and Dempster–Shafer Theory

  • Ronald R. Yager


Human behavioral modeling requires an ability to represent and manipulate imprecise cognitive concepts. It also needs to include the uncertainty and unpredictability of human action. We discuss the appropriateness of fuzzy sets for representing human centered cognitive concepts. We describe the technology of fuzzy systems modeling and indicate its the role in human behavioral modeling. We next introduce some ideas from the Dempster-Shafer theory of evidence. We use the Dempster-Shafer theory to provide a machinery for including randomness in the fuzzy systems modeling process. This combined methodology provides a framework with which we can construct models that can include both the complex cognitive concepts and unpredictability needed to model human behavior.


Interest Rate Multiple Input Output Fuzzy Subset Membership Grade Belief Structure 
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. 1.
    Zadeh, L. A., “A note on web intelligence, world knowledge and fuzzy logic,” Data and Knowledge Engineering 50, 291-304, 2004.CrossRefGoogle Scholar
  2. 2.
    Yager, R. R., “Using knowledge trees for semantic web querying,” in Fuzzy Logic and the Semantic Web, edited by Sanchez, E., Elsevier: Amsterdam, 231-246, 2006.Google Scholar
  3. 3.
    Scott, J., Social Network Analysis, SAGE Publishers: Los Angeles, 2000.Google Scholar
  4. 4.
    Pedrycz, W. and Gomide, F., Fuzzy Systems Engineering: Toward Human-Centric Computing, John Wiley & Sons: New York, 2007.Google Scholar
  5. 5.
    Sugeno, M., Industrial Applications of Fuzzy Control, North-Holland: Amsterdam, 1985.Google Scholar
  6. 6.
    Shafer, G., A Mathematical Theory of Evidence, Princeton University Press: Princeton, N.J., 1976.MATHGoogle Scholar
  7. 7.
    Yager, R. R. and Liu, L., (A. P. Dempster and G.Shafer, Advisory Editors) Classic Works of the Dempster-Shafer Theory of Belief Functions, Springer: Heidelberg, (To Appear).Google Scholar
  8. 8.
    Lin, T. S., Yao, Y. Y. and Zadeh, L. A., Data Mining, Rough Sets and Granular Computing, Physica-Verlag: Heidelberg, 2002.MATHGoogle Scholar
  9. 9.
    Bargiela, A. and Pedrycz, W., Granular Computing: An Introduction, Kluwer Academic Publishers: Amsterdam, 2003.MATHGoogle Scholar
  10. 10.
    Yager, R. R. and Filev, D. P., Essentials of Fuzzy Modeling and Control, John Wiley: New York, 1994.Google Scholar
  11. 11.
    Yager, R. R., “On the retranslation process in Zadeh’s paradigm of computing with words,” IEEE Transactions on Systems, Man and Cybernetics: Part B 34, 1184-1195, 2004.Google Scholar
  12. 12.
    Yager, R. R., “Arithmetic and other operations on Dempster-Shafer structures,” Int. J. of Man-Machine Studies 25, 357-366, 1986.MathSciNetCrossRefGoogle Scholar
  13. 13.
    Yager, R. R., “Entropy and specificity in a mathematical theory of evidence,” Int. J. of General Systems 9, 249-260, 1983.CrossRefMathSciNetMATHGoogle Scholar
  14. 14.
    Yen, J., “Generalizing the Dempster-Shafer theory to fuzzy sets,” IEEE Transactions on Systems, Man and Cybernetics 20, 559-570, 1990.Google Scholar
  15. 15.
    Dubois, D. and Prade, H., “Possibility theory as a basis for qualitative decision theory,“ Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Montreal, 1924-1930, 1995.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Ronald R. Yager
    • 1
  1. 1.Machine Intelligence Institute Iona CollegeNew Rochelle

Personalised recommendations