Advertisement

Behavior Clustering and Explicitation for the Study of Agents’ Credibility: Application to a Virtual Driver Simulation

  • Kévin DartyEmail author
  • Julien Saunier
  • Nicolas Sabouret
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8946)

Abstract

The aim of this article is to provide a method for evaluating the credibility of agents’ behaviors in immersive multi-agent simulations. It is based on a quantitative data collection from both humans and agents simulation logs during an experiment. These data allow us to semi-automatically extract behavior clusters. In order to obtain explicit information about the behaviors, we analyze questionnaires filled by the users and annotations filled by a second set of participants. It enables to draw user categories related to their behavior in the context of the simulation or of their real life habits. We then study the similarities between behavior clusters, user categories, and participants’ annotations. Afterwards, we evaluate the agents’ credibility and make their behaviors explicit by comparing human behaviors to agent ones according to user categories and annotations. Our method is applied to the study of virtual driver simulation through an immersive driving simulator.

Keywords

Multi-agent simulation Behavior clustering Credibility evaluation 

References

  1. 1.
    Arthur, D., Vassilvitskii, S.: k-means++: The advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics (2007)Google Scholar
  2. 2.
    Berndt, D., Clifford, J.: Using dynamic time warping to find patterns in time series. KDD workshop. 10, 359–370 (1994)Google Scholar
  3. 3.
    Burkhardt, J.M., Bardy, B., Lourdeaux, D.: Immersion, réalisme et présence dans la conception et l’évaluation des environnements virtuels. Psychol. française 48(2), 35–42 (2003)Google Scholar
  4. 4.
    Caillou, P., Gil-Quijano, J.: Simanalyzer: Automated description of groups dynamics in agent-based simulations. In: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, Vol. 3, pp. 1353–1354. International Foundation for Autonomous Agents and Multiagent Systems (2012)Google Scholar
  5. 5.
    Caliński, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat.-Theory Methods 3(1), 1–27 (1974)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Champion, A., Éspié, S., Auberlet, J.M.: Behavioral road traffic simulation with archisim. In: Summer Computer Simulation Conference, pp. 359–364. Society for Computer Simulation International (1998, 2001)Google Scholar
  7. 7.
    Champion, A., Zhang, M.Y., Auberlet, J.M., Espié, S.: Behavioral simulation: Towards high-density network traffic studies. In: ASCE (2002)Google Scholar
  8. 8.
    Drogoul, A., Corbara, B., Fresneau, D.: Manta New experimental results on the emergence of (artificial) ant societies. In: Gilbert, N., Conte, R. (eds.) Artificial Societies the Computer Simulation of Social Life, pp. 190–211. UCL Press, London (1995)Google Scholar
  9. 9.
    Fontaine, G.: The experience of a sense of presence in intercultural and international encounters. Presence: Teleoperators Virtual Environ. 1(4), 482–490 (1992)CrossRefGoogle Scholar
  10. 10.
    Gonçalves, J., Rossetti, R.J.F.: Extending sumo to support tailored driving styles. In: 1st SUMO User Conference, DLR, Berlin Adlershof, Germany, vol. 21, pp. 205–211 (2013)Google Scholar
  11. 11.
    Hubert, L., Arabie, P.: Comparing partitions. J. classif. 2(1), 193–218 (1985)CrossRefzbMATHGoogle Scholar
  12. 12.
    Javeau, C.: L’enquête par questionnaire: manuel à l’usage du praticien. Editions de l’Université de Bruxelles (1978)Google Scholar
  13. 13.
    Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)CrossRefGoogle Scholar
  14. 14.
    Leplat, J.: Simulation et simulateur: principes et usages. Regards sur l’activité en situation de travail: contribution à la psychologie ergonomique, pp. 157–181 (1997)Google Scholar
  15. 15.
    Lessiter, J., Freeman, J., Keogh, E., Davidoff, J.: A cross-media presence questionnaire: the itc-sense of presence inventory. Presence: Teleoperators Virtual Environ. 10(3), 282–297 (2001)CrossRefGoogle Scholar
  16. 16.
    Lester, J.C., Converse, S.A., et al.: The persona effect: affective impact of animated pedagogical agents. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 359–366. ACM (1997)Google Scholar
  17. 17.
    Likert, R.: A technique for the measurement of attitudes. Arch. psychol. 22, 1–55 (1932)Google Scholar
  18. 18.
    Maes, P., Kozierok, R.: Learning interface agents. In: Proceedings of the National Conference on Artificial Intelligence, pp. 459–459. Wiley (1993)Google Scholar
  19. 19.
    McGreevy, M.W.: The presence of field geologists in mars-like terrain. Presence: Teleoperators and Virtual Environ. 1(4), 375–403 (1992)CrossRefGoogle Scholar
  20. 20.
    Milligan, G.W., Cooper, M.C.: A study of the comparability of external criteria for hierarchical cluster analysis. Multivar. Behav. Res. 21(4), 441–458 (1986)CrossRefGoogle Scholar
  21. 21.
    Patrick, J.: Training: Research and Practice. Academic Press, London (1992)Google Scholar
  22. 22.
    Pavlov, I.P., Anrep, G.V.: Conditioned reflexes. Dover Publications, New York (2003)Google Scholar
  23. 23.
    Pelleg, D., Moore, A., et al.: X-means: Extending k-means with efficient estimation of the number of clusters. In: Proceedings of the Seventeenth International Conference on Machine Learning, vol. 1, pp. 727–734. San Francisco (2000)Google Scholar
  24. 24.
    Premack, D., Woodruff, G., et al.: Does the chimpanzee have a theory of mind. Behav. Brain Sci. 1(4), 515–526 (1978)CrossRefGoogle Scholar
  25. 25.
    Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846–850 (1971)CrossRefGoogle Scholar
  26. 26.
    Reason, J., Manstead, A., Stradling, S., Baxter, J., Campbell, K.: Errors and violations on the roads: a real distinction? Ergonomics 33(10–11), 1315–1332 (1990)CrossRefGoogle Scholar
  27. 27.
    Salvador, S., Chan, P.: Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5), 561–580 (2007)Google Scholar
  28. 28.
    Serrano, E., Muñoz, A., Botia, J.: An approach to debug interactions in multi-agent system software tests. Inf. Sci. 205, 38–57 (2012)CrossRefGoogle Scholar
  29. 29.
    Stoffregen, T.A., Bardy, B.G., Smart, L.J., Pagulayan, R.J.: On the nature and evaluation of fidelity in virtual environments. In: Hettinger, L.J., Hass, M.W. (eds.) Virtual and Adaptive Environments Applications, Implications, and Human Performance Issues, pp. 111–128. Erlbaum, Mahwah (2003)CrossRefGoogle Scholar
  30. 30.
    Witmer, B.G., Singer, M.J.: Measuring presence in virtual environments: a presence questionnaire. Presence 7(3), 225–240 (1998)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Kévin Darty
    • 1
    Email author
  • Julien Saunier
    • 2
  • Nicolas Sabouret
    • 3
  1. 1.Laboratory for Road Operations, Perception, Simulators and SimulationsFrench Institute of Science and Technology for Transport, Development and NetworksMarne la ValléeFrance
  2. 2.Computer Science, Information Processing and Systems LaboratoryNational Institute of Applied Sciences of RouenSaint-étienne-du-rouvray CedexFrance
  3. 3.Computer Sciences Laboratory for Mechanics and Engineering Sciences, National Center for Scientific ResearchParis-Sud UniversityOrsay CedexFrance

Personalised recommendations