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Fuzzy Emotion Model for Affective Computing Agents

감성 에이전트를 위한 퍼지 정서 모델

  • Yoon, Hyun Joong (School of Mechanical and Automotive Engineering, Catholic University of Daegu) ;
  • Chung, Seong Youb (Department of Mechanical Engineering, Korea National University of Transportation)
  • 윤현중 (대구가톨릭대학교 기계자동차공학부) ;
  • 정성엽 (한국교통대학교 기계공학과)
  • Received : 2014.02.28
  • Accepted : 2014.10.14
  • Published : 2014.12.31

Abstract

This paper addresses the emotion computing model for software affective agents. In this paper, emotion is represented in valence-arousal-dominance dimensions instead of discrete categorical representation approach. Firstly, a novel emotion model architecture for affective agents is proposed based on Scherer's componential theories of human emotion, which is one of the well-known emotion models in psychological area. Then a fuzzy logic is applied to determine emotional statuses in the emotion model architecture, i.e., the first valence and arousal, the second valence and arousal, and dominance. The proposed methods are implemented and tested by applying them in a virtual training system for children's neurobehavioral disorders.

Keywords

References

  1. Arnold, M.B. and Gasson, S.J., Feelings and Emotions as Dynamic Factors in Personality Integration, In M.B. Arnold (Ed.), The Human Person : An Approach to an Integral Theory of Personality, New York : Ronald Press, 1954.
  2. Barrett, L.F., Discrete Emotions or Dimensions? The Role of Valence Focus and Arousal Foc. Cognition and Emotion, 1998, Vol. 12, No. 4, p 579-599. https://doi.org/10.1080/026999398379574
  3. Becker-Asano, C., WASABI : Affect Simulation for Agents with Believable Interactivity, Universitat Bielefeld, Ph. D. Dissertation, 2008.
  4. Bradley, M.M. and Lang, P.J., Affective Norms for English Words(ANEW) : Instruction Manual and Affec tive Ratings, Technical Report C-1, The Center for Research in Psychophysiology, University of Florida, 1999.
  5. Chung, S.Y. and Yoon, H.J., A Framework for Treatment of Autism Using Affective Computing, In Proceedings of the Medicine Meets Virtual Reality(MMVR) 18, California, USA, February 8-12, 2011.
  6. Chung, S.Y. and Yoon, H.J., Emotion Classification Using EEG Spectrum Analysis and Bayesian Approach. Journal of the Society of Korea Industrial and Systems Engineering, to be published.
  7. Dautenhahn, K., "The Art of designing socially intelligent agents : Science, fiction and the human in the loop. Applied Artificial Intelligence Journal. Special Issue on Socially Intelligent Agents, 1998, Vol. 12, No. 7-8, p 573- 619.
  8. El-Nasr, M.S., Yen, J., and Ioerger, T.R., "FLAMEFuzzy Logic Adaptive Model of Emotions. Autonomous Agents and Multi-Agent Systems, 2000, Vol. 3, No. 3, p 219-257. https://doi.org/10.1023/A:1010030809960
  9. Elliot, C., The Affective Reasoner : A Process Model of Emotions in a Multi-Agent System, Institute for the Learning Sciences, Evanston, IL : Northwestern University, Ph.D. Dissertation, 1992.
  10. Frijda, N.H. and Swagerman, J., Can Computers Feel? Theory and Design of an Emotional System. Cognition and Emotion, 1987, Vol. 1, No. 3, p 235-257. https://doi.org/10.1080/02699938708408050
  11. Gratch, J. and Marsella, S., A Domain-Independent Framework for Modeling Emotion. Journal of Cognitive Systems Research, 2004, Vol. 5, No. 4, p 269-306. https://doi.org/10.1016/j.cogsys.2004.02.002
  12. Maes, P., Artificial life meets entertainment : Lifelike autonomous agents. Communications of the ACM Special Issue on Novel Applications of AI, 1995, Vol. 38, No. 11, p 108-114.
  13. Marsella, S.C. and Gratch, J., "EMA : A Process Model of Appraisal Dynamics. Cognitive Systems Research, 2009, Vol. 10, p 70-90. https://doi.org/10.1016/j.cogsys.2008.03.005
  14. Oatley, K. and Jenkins, J.M., Understanding Emotions. Backwell, 1996.
  15. Ortony, A., Clore, G., and Collins, A., The Cognitive Structure of Emotions, Cambridge : Cambridge University Press, 1988.
  16. Reilly, W.S., Believable Social and Emotional Agents, Ph.D. Dissertation, School of Computer Science, Carnegie Mellon University, 1996.
  17. Rizzo, A.A., Klimchuk, D., Mitura, R., Bowerly, T., Buckwalter, G.B., Kerns, K., Randall, K., Adams, R., Finn, P., Tarnanas, I., Sirbu, C., Ollendick, T.H., and Yeh, S.C., A virtual reality scenario for all seasons : The virtual classroom, in Proceedings of the 11th International Conference on Human Computer Interaction, Las Vegas, USA, 2005, p 22-27.
  18. Roseman, I.J., Jose, P.E., and Spindel, M.S., Appraisals of Emotion-Eliciting Events : Testing a Theory of Discrete Emotions. Journal of Personality and Social Psychology, 1990, Vol. 59, No. 5, p 899-915. https://doi.org/10.1037/0022-3514.59.5.899
  19. Sander, D., Grandjean, D., and Scherer, K.R., A System Approach to Appraisal Mechanisms in Emotion. Neural Network, 2005, Vol. 18, p 317-352. https://doi.org/10.1016/j.neunet.2005.03.001
  20. Scherer, K., "Toward a Dynamic Theory of Emotion : The Component Process Model of Affective States. Geneva Studies in Emotion and Communication, 1987, Vol. 1, No. 1, p 1-98.
  21. Scherer, K.R., Appraisal Considered as a Process of Multi-Level Sequential Checking, In K.R. Scherer, A. Schorr, and T. Johnstone (Eds.), Appraisal Processes in Emotion : Theory, Methods, Research (p 92-120). New York : Oxford University Press, 2001, p 92-120.
  22. Shibata, T., Inoue, K., and Irie, R., Emotional robot for intelligent system-Artificial emotional creature project. IEEE International Workshop on Robot and Human Communication, Tokyo, Japan, 1996, p 466-471.
  23. Wundt, W., Grundzuge der Physologischen Psychologie. Leipzig : Engelmann, 1905.
  24. Yoon, H.J. and Chung, S.Y., EEG-Based Emotion Estimation Using Bayesian Weighted-Log-Posterior Function and Perceptron Convergence Algorithm. Computers in Biology and Medicine, Vol. 43, No. 12, 2013, p 2230-2237. https://doi.org/10.1016/j.compbiomed.2013.10.017