Applying Data Fusion in a Rational Decision Making with Emotional Regulation

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4850)


This paper focuses on designing a goal based rational component of a believable agent which has to interact with facial expressions with humans in communicative scenarios like teaching. One of the main concerns of the proposed model is to define interactions among rationality, personality and emotion in order to fulfill the idea of making rational decisions with emotional regulation. Our research aims are directed towards improving decision making process by means of applying Data Fusion techniques, especially Ordered Weighted Averaging (OWA) operator as a goal selection mechanism. Also the issue of obtaining weights for OWA aggregation is discussed. Finally the suggested algorithm is tested and results are provided with a real benchmark.


Data Fusion OWA Rationality Artificial Emotions Decision Making 


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  1. 1.
    Abidi, M.A., Gonzalez, R.C.: Data Fusion in robotics and machine intelligence. Academic Press, San Diego (1992)zbMATHGoogle Scholar
  2. 2.
    Beliakov, G.: How to Build Aggregation Operators from Data. International Journal of Intelligent Systems 18, 903–923 (2003)zbMATHCrossRefGoogle Scholar
  3. 3.
    Boutilier, C., Dean, T., Hanks, S.: Decision-theoretic planning: Structural assumptions and computational leverage. Journal of Artificial Intelligence Research 11, 1–94 (1999)zbMATHMathSciNetGoogle Scholar
  4. 4.
    Breazeal, C.L.: Emotion and Sociable Humanoid Robots. International Journal of Human-Computer Studies 59, 119–155 (2003)CrossRefGoogle Scholar
  5. 5.
    Camurri, A., Caglio, A.: An Architecture for Emotional Agents. IEEE Multimedia Journal (1998)Google Scholar
  6. 6.
    Camurri, A., Volpe, G.: A Goal Directed Rational Component for Emotional Agent. Systems, Man, and Cybernetics. In: IEEE SMC 1999 Conference Proceedings, IEEE Computer Society Press, Los Alamitos (1999)Google Scholar
  7. 7.
    Filev, D.P., Yager, R.R.: Learning OWA Operator Weights From Data. In: Proceedings of the Third IEEE International Conference on Fuzzy Systems, Orlando, pp. 468–473. IEEE Computer Society Press, Los Alamitos (1994)CrossRefGoogle Scholar
  8. 8.
    Filev, D., Yager, R.: On the issue of obtaining OWA operator weights. Fuzzy Set Systems 94, 157–169 (1998)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Fuller, R., Majlender, P.: An analytic approach for obtaining maximal entropy OWA operator weights. Fuzzy Set Systems 124, 53–57 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Fuller, R., Majlender, P.: On obtaining minimal variability OWA operator weights. Fuzzy Sets and Systems 136(2), 203–215 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Hall, D.L., LLinas, J.: Handbook of Multisensor Data Fusion. CRC Press, Boca Raton (2001)Google Scholar
  12. 12.
    Millon, T., Lerner, M.J.: Handbook of Psychology. Personality and social psychology 5 (2003)Google Scholar
  13. 13.
    Sartre, J.P.: Being and Nothingness. Trans. Hazel E. Barnes. Quokka Books, New York (1978)Google Scholar
  14. 14.
    Scassellati, B.M.: Foundations for a Theory of Mind for a Humanoid Robot. MIT University (2001)Google Scholar
  15. 15.
    Yager, R.R.: On Ordered Weighted Averaging Operators in Multicriteria Decision Making. IEEE Transaction on Systems, Man and Cybernetics (1988)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  1. 1.Young Researchers Club, TehranIran
  2. 2.Control and Intelligent Processing, Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, TehranIran

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