Advertisement

A Computational Behavior Model for Life-Like Intelligent Agents

  • Mohammadreza AlidoustEmail author
  • Modjtaba Rouhani
Chapter
  • 2.5k Downloads
Part of the Topics in Intelligent Engineering and Informatics book series (TIEI, volume 9)

Abstract

In this paper a novel computational behavior model is proposed which has a simple structure and also includes some of the major affecting parameters to the decision making process such as the agent’s emotions, personality, intelligence level and physical situation. The effect of these parameters has been studied and the model has been simulated in a goal-achieving scenario for four agents with different characteristics. Simulation results show that the behavior of these intelligent agents are natural and believable and suggest that this model can be used as the decision making and behavior control unit of future life-like intelligent agents.

Keywords

intelligent agent behavior modeling decision making emotion modeling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lee, S., Son, Y.: Integrated human decision mmaking model under belief-desire-intention framework for crowd simulation. In: Proceedings of the 2008 Winter Simulation Conference (2008)Google Scholar
  2. 2.
    Opaluch, J.J., Segerson, K.: Rational roots of irrational behavior: New theories of economic decision-making. Northeastern Journal of Agricultural and Resource Economics 18(2), 81–95 (1989)Google Scholar
  3. 3.
    Gibson, F.P., Fichman, M., Plaut, D.C.: Learning in dynamic decision tasks: Computational model and empirical evidence. Organizational Behavior and Human Decision Processes 71, 1–35 (1997)CrossRefGoogle Scholar
  4. 4.
    Einhorn, H.J.: The use of nonlinear, noncompensatory models in decision making. Psychological Bulletin 73, 221–230 (1970)CrossRefGoogle Scholar
  5. 5.
    Payne, J.W.: Contingent decision behavior. Psychological Bulletin 92, 382–402 (1982)CrossRefGoogle Scholar
  6. 6.
    Busemeyer, J.R., Townsend, J.T.: Decision field theory: A dynamic-cognitive approach to decision making in an uncertain environment. Psychological Review 100(3), 432–459 (1993)CrossRefGoogle Scholar
  7. 7.
    Laird, J.E., Newell, A., Rosenbloom, P.S.: Soar: An architecture for general intelligence. Artificial Intelligence 33, 1–64 (1987)CrossRefGoogle Scholar
  8. 8.
    Newell, A.: Unified Theories of Cognition. Harvard University Press, Cambridge (1990)Google Scholar
  9. 9.
    Rao, A., Georgeff, M.: Decision procedures for bdi logics. Journal of logic and Computation 8, 293–342 (1998)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Konar, A., Chakraborty, U.K.: Reasoning and unsupervised learning in a fuzzy cognitive map. Information Sciences 170 (2005)Google Scholar
  11. 11.
    Zhao, X., Son, Y.: Bdi-based human decision- making model in automated manufacturing systems. International Journal of Modeling and Simulation (2007)Google Scholar
  12. 12.
    Rothrock, L., Yin, J.: Integrating compensatory and noncompensatory decision making strategies in dynamic task environments. In: Decision Modeling and Behavior in Uncertain and Complex Environments, pp. 123–138 (2008)Google Scholar
  13. 13.
    Lee, S., Son, Y., Jin, J.: Decision field theory extensions for behavior modeling in dynamic environment using bayesian belief network. Information Sciences 178(10), 2297–2314 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  14. 14.
    Ortony, A., Clore, G., Collins, A.: The Cognitive Structure of Emotions. Cambridge University Press, Cambridge (1988)CrossRefGoogle Scholar
  15. 15.
    Gomi, T., Vardalas, J., Koh-Ichi, I.: Elements of artificial emotion. In: Robot and Human Communication, pp. 265–268 (1995)Google Scholar
  16. 16.
    Kort, B., Reilly, R., Picard, R.: An affective model of interplay between emotions and learning. In: Proceedings of IEEE International Conference on Advanced Learning Technologies, pp. 43–46 (2001)Google Scholar
  17. 17.
    Picard, R., Vyzas, E., Healey, J.: Toward machine emotional intelligence-analysis of affective physiological state. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 1175–1191 (2001)CrossRefGoogle Scholar
  18. 18.
    Seif El-Nasr, M., Yen, J., Ioerger, T.: Flame-fuzzy logic adaptive model of emotion. International Journal of Autonomous Agents and Multi-Agent Systems (2000)Google Scholar
  19. 19.
    Hidenori, I., Fukuda, T.: Individuality of agent with emotional algorithm. In: Proceedings of IEEE 2001 International Conference on Intelligent Robots and Systems, pp. 1195–1200 (2001)Google Scholar
  20. 20.
    Wang, Z., Qiao, X., Wang, C., Yu, J., Xie, L.: Research on emotion modeling based on custom space and occ model. Computer Engineering 33(4), 189–192 (2007)Google Scholar
  21. 21.
    Zhenlong, L., Xiaoxia, W.: Emotion modeling of the driver based on fuzzy logic. In: 12th International IEEE Conference on intelligent Transportation Systems (2009)Google Scholar
  22. 22.
    Chakraborty, A., Konar, A., Chakraborty, U.K., Chatterjee, A.: Emotion recognition from facial expressions and its control using fuzzy logic. IEEE Transactions on Systems, Man, and Cybernetics (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Islamic Azad University – Science and Research BranchTehranIran
  2. 2.Ferdowsi UniversityMashhadIran

Personalised recommendations