Agent Behaviour Modeling Using Personality Profile Characterization for Emergency Evacuation Serious Games

  • César García-García
  • Victor Larios-Rosillo
  • Hervé Luga
Part of the Studies in Computational Intelligence book series (SCI, volume 441)

Abstract

This chapter presents a current effort to model agent-personality using personality profile data extracted from individuals in the population. The obtained profiles are then used in a personality-driven behaviour engine to create realistic scenarios where massive evacuations can be simulated to promote awareness of the risks and emergency procedures in said scenarios.

Keywords

Multiagent System Emotional Stability Computer Animation Smart Object Emergency Evacuation 
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.

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References

  1. 1.
    Anderson, J.R.: Spanning seven orders of magnitude: a challenge for cognitive modeling. Cognitive Science 26, 85–112 (2002)CrossRefGoogle Scholar
  2. 2.
    Barrett, P., Hutton, R.: The distortion of meaning and measurement in applicant sample personality questionnaire responses. In: British Psychological Society (ed.) BPS Occupational Psychology Proceedings (2000)Google Scholar
  3. 3.
    Bates, J.: The role of emotion in believable agents. Communications of the ACM 37(7), 122–125 (1994)CrossRefGoogle Scholar
  4. 4.
    Bellifemine, F., Caire, G., Poggi, A., Rimassa, G.: Java Agent DEvelopment Framework: A White Paper (2003)Google Scholar
  5. 5.
    Blumberg, B.M., Galyean, T.A.: Multi-level direction of autonomous creatures for real-time virtual environments. In: SIGGRAPH 1995: Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, pp. 47–54. ACM, New York (1995)CrossRefGoogle Scholar
  6. 6.
    Cattell, R.: The description and measurement of personality. Harcourt, Brace and World, New York (1946)Google Scholar
  7. 7.
    Cattell, R.: Personality and motivation structure and measurement. World Book (1957)Google Scholar
  8. 8.
    Conn, S.R., Rieke, M.L.: The 16PF Fifth Edition technical manual. Institute for Personality and Ability Testing, Champaign (1994)Google Scholar
  9. 9.
    Durupinar, F., Allbeck, J., Pelechano, N., Badler, N.I.: Creating crowd variation with ocean personality model. In: Padgham, L., Parkes, Müller, Parsons (eds.) Proc. of 7th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2008), Estoril, Portugal, pp. 12–16. International Foundation for Autonomous Agents and Multiagent Systems (2008)Google Scholar
  10. 10.
    Durupinar, F., Pelechano, N., Allbeck, J., Güdükbay, U., Badler, N.I.: How the ocean personality model affects the perception of crowds. In: IEEE Computer Graphics and Applications (May 2011)Google Scholar
  11. 11.
    Edward, L., Lourdeaux, D., Barthes, J.-P.: Cognitive modeling of virtual autonomous intelligent agents integrating human factors. In: IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, pp. 353–356 (2009)Google Scholar
  12. 12.
    García-García, C., Torres-López, L., Larios-Rosillo, V., Luga, H.: A GOAP architecture for emergency evacuations in serious games. In: GAME-ON 2010: 11th International Conference on Intelligent Games and Simulation, Leicester, UK, pp. 10–12 (November 2010)Google Scholar
  13. 13.
    García-García, C., Torres-Nabel, C., Larios-Rosillo, V., Luga, H.: Towards a Behavioral Model for Personality-driven Artificial Humanoids in Serious Games. In: Plemenos, D. (ed.) 14th International Conference 3IA 2011, the International Conference in Computer Graphics and Artificial Intelligence, Athens, Greece, pp. 131–136 (May 2011)Google Scholar
  14. 14.
    Guy, S.J., Kim, S., Lin, M.C., Manocha, D.: Simulating heterogeneous crowd behaviors using personality trait theory. In: Bargteil, A., van de Panne, M. (eds.) ACM SIGGRAPH Symposium on Computer Animation 2011 (2011)Google Scholar
  15. 15.
    Lee, S., Son, Y.-J.: Integrated human decision making model under belief-desire-intention framework for crowd simulation. In: Mason, S.J., Hill, R.R., Mönch, L., Rose, O., Jefferson, T., Fowler, J.W. (eds.) Proceedings of the 2008 Winter Simulation Conference, pp. 886–894 (2008)Google Scholar
  16. 16.
    Ruvalcaba, F.M.: Estrategias para aprender que utilizan los estudiantes de bachillerato. PhD thesis, Universidad de Guadalajara, Guadalajara, México (August 2011)Google Scholar
  17. 17.
    McCrae, R.R., Costa, P.T.: Aging, the life course, and models of personality. In: Review of Human Development. Wiley (1982)Google Scholar
  18. 18.
    Medina-Mora, M.E., Borges, G., Muñoz, C.L., Benjet, C., Jaimes, J.B., Bautista, C.F., Velázquez, J.V., Guiot, E.R., Ruíz, J.Z., Rodas, L.C., Aguilar-Gaxiola, S.: Prevalencia de trastornos mentales y uso de servicios: Resultados de la encuesta nacional de epidemiología psiquiátrica en México. Salud Mental 26(4), 1–16 (2003)Google Scholar
  19. 19.
    Montgomery, D.C.: Design and Analysis of Experiments, 5th edn. John Wiley and Sons, Inc. (2001)Google Scholar
  20. 20.
    Orkin, J.: AI Game Programming Wisdom 2. Charles River Media (2003)Google Scholar
  21. 21.
    Orkin, J.: Symbolic representation of game world state: Toward real-time planning in games. In: AAAI Workshop (2004)Google Scholar
  22. 22.
    Pelechano, N., Allbeck, J., Badler, N.I.: Controlling individual agents in high-density crowd simulation. In: Metaxas, D., Popovic, J. (eds.) ACM SIGGRAPH Symposium on Computer Animation (2007), California, US (2007)Google Scholar
  23. 23.
    Pelechano, N., Badler, N.I.: Modeling crowd and trained leader behavior during building evacuation. IEEE Computer Graphics and Applications (2006)Google Scholar
  24. 24.
    Perlin, K., Goldberg, A.: Improv: a system for scripting interactive actors in virtual worlds. In: SIGGRAPH 1996: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, pp. 205–216. ACM, New York (1996)CrossRefGoogle Scholar
  25. 25.
    Poropat, A.E.: A meta-analysis of the five-factor model of personality and academic performance. Psychological Bulletin 135(2), 322–338 (2009)CrossRefGoogle Scholar
  26. 26.
    Powell, M.: JMonkeyEngine (2003)Google Scholar
  27. 27.
    Shao, W., Terzopoulos, D.: Autonomous pedestrians. In: SCA 2005: Proceedings of the 2005 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 19–28. ACM, New York (2005)CrossRefGoogle Scholar
  28. 28.
    Skinner, B.F.: Science and Human Behavior. The Macmillan Company (1953)Google Scholar
  29. 29.
    Telecom Italia. Java agent development framework (April 2001)Google Scholar
  30. 30.
    UN World Health Organization. Actualización de la Clasificación Internacional de Enfermedades, Décima Revisión (CIE-10) (June 2003)Google Scholar
  31. 31.
    Wiggins, J.S.: The Five-Factor Model of Personality: Theoretical Perspectives. The Guilford Press, New York (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • César García-García
    • 1
  • Victor Larios-Rosillo
    • 1
  • Hervé Luga
    • 2
  1. 1.Universidad de GuadalajaraJaliscoMéxico
  2. 2.Université Toulouse 1ToulouseFrance

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