Human-in-the-Loop Simulation of a Virtual Classroom

  • Jesper Nilsson
  • Franziska Klügl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9571)


As technology for virtual reality becomes more and more accessible, virtual reality based training becomes a hot topic as an application environment for multiagent systems. In this contribution, we present a system that connects a game engine with a BDI platform for simulating a group of listeners that may be believable enough for serving as a virtual audience for training gestures and body language while teaching.


Virtual Reality Multiagent System Agent Behaviour Cognitive Architecture Virtual Character 
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.



The authors would like to thank Elisabeth André and her group at the University of Augsburg - in particular Michael Wissner - for their support with the Horde 3D Game Engine as well as with providing the 3d Object models for the virtual students and the scenario visualization.


  1. 1.
    Adam, C., Lorini, E.: A BDI emotional reasoning engine for an artificial companion. In: Corchado, J.M., et al. (eds.) PAAMS 2014. CCIS, vol. 430, pp. 66–78. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  2. 2.
    Anderson, J.R.: Rules of the Mind. Erlbaum, Hillsdale (1993)Google Scholar
  3. 3.
    Anderson, K., et al.: The TARDIS framework: intelligent virtual agents for social coaching in job interviews. In: Reidsma, D., Katayose, H., Nijholt, A. (eds.) ACE 2013. LNCS, vol. 8253, pp. 476–491. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Antunes, L., Nunes, D., Coelho, H.: The geometry of desire. In: Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014, Paris, France, pp. 1169–1172, May 2014 (2014)Google Scholar
  5. 5.
    Avradinis, N., Panayiotopoulos, T., Anastassakis, G.: Behavior believability in virtual worlds: agents acting when they need to. SpringerPlus 2, 246 (2013)CrossRefGoogle Scholar
  6. 6.
    Bach, J.: MicroPsi 2: the next generation of the MicroPsi framework. In: Bach, J., Goertzel, B., Iklé, M. (eds.) AGI 2012. LNCS, vol. 7716, pp. 11–20. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Bates, J., Lyall, A.B., Reilly, W.S.: An architecture for action, emotion, social behavior. In: Castelfranchi, C., Werner, E. (eds.) MAAMAW 1992. LNCS, vol. 830, pp. 55–68. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  8. 8.
    Bordini, R.H., Hübner, J.F., Wooldridge, M.: Programming Multi-agent Systems in AgentSpeak using Jason: A Practical Introduction with Jason. Wiley, Chichester (2007)CrossRefzbMATHGoogle Scholar
  9. 9.
    Cai, Z., Goertzel, B., Zhou, C., Huang, D., Ke, S., Yu, G., Jiang, M.: OpenPsi: a novel computational affective model and its application in video games. Eng. Appl. Artif. Intell. 26, 1–12 (2013)CrossRefGoogle Scholar
  10. 10.
    Dörner, D.: Bauplan für eine Seele. Rowohlt, Reinbeck (1999)Google Scholar
  11. 11.
    Eschenbrenner, B., Nah, F.F., Siau, K.: 3-d virtual worlds in education: applications, benefits, issues, and opportunities. J. Database Manag. 19(4), 91–110 (2008)CrossRefGoogle Scholar
  12. 12.
    Hayes-Roth, B.: What makes characters seem life-like. In: Prendinger, H., Ishizuka, M. (eds.) Life-Like Characters, Cognitive Technologies, pp. 447–462. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Ingrand, F.F., Georgeff, M.P., Rao, A.S.: An architecture for real-time reasoning and system control. IEEE Expert Intell. Syst. Appl. 7(6), 34–44 (1992)Google Scholar
  14. 14.
    Klügl, F., Wissner, M., Timpf, S., Andre, E.: Bridging virtual world visualization and multiagent simulation (extended abstract). In: Proceedings of the 11th Scandinavian Conference on Artificial Intelligence, Trondheim, May 2011, pp. 191–192 (2011)Google Scholar
  15. 15.
    Laird, J.E., Duchi, J.C.: Creating human-like synthetic characters with multiple skill levels: a case study using the soar quakebot. In: AAAI 2000 Fall Symposium Series: Simulating Human Agents, November 2000 (2000)Google Scholar
  16. 16.
    Lee, J., Baines, V., Padget, J.: Decoupling cognitive agents and virtual environments. In: Dignum, F., Brom, C., Hindriks, K., Beer, M., Richards, D. (eds.) CAVE 2012. LNCS, vol. 7764, pp. 17–36. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  17. 17.
    Louloudi, A., Klügl, F.: Immersive face validation: a new validation technique for agent-based simulation. In: Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1255–1260 (2012)Google Scholar
  18. 18.
    McCrae, R.R., John, O.P.: An introduction to the five-factor model and its application. J. Personality 60(2), 175–215 (1992)CrossRefGoogle Scholar
  19. 19.
    Norling, E.: On evaluating agents for serious games. In: Dignum, F., Bradshaw, J., Silverman, B., van Doesburg, W. (eds.) Agents for Games and Simulations. LNCS, vol. 5920, pp. 155–169. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  20. 20.
    Ortony, A., Clore, G.L., Collins, A.: The Cognitive Structure of Emotions. Cambridge University Press, Cambridge (1988)CrossRefGoogle Scholar
  21. 21.
    Parunak, H.V.D., Bisson, R., Brueckner, S., Matthews, R., Sauter, J.: A model of emotions for situated agents. In: Proceedings of AAMAS, Hokodate, Japan, pp. 993–996 (2006)Google Scholar
  22. 22.
    Poeschl, S., Doering, N.: Virtual training for fear of public speaking–design of an audience for immersive virtual environments. In: Virtual Reality Short Papers and Posters (VRW), pp. 101–102. IEEE (2012)Google Scholar
  23. 23.
    Prada, L., Paiva, A.: Teaming up humans with autonomous synthetic characters. Artif. Intell. 173, 80–103 (2009)CrossRefGoogle Scholar
  24. 24.
    Prendinger, H., Ishizuka, M. (eds.): Life-Like Characters. Springer, Heidelberg (2004)Google Scholar
  25. 25.
    Slater, M., Pertaub, D.-P., Barker, C., Clark, D.: An experimental study on fear of public speaking using a virtual environment. Cyberpsychol. Behav. 9(5), 627–633 (2006)CrossRefGoogle Scholar
  26. 26.
    Steunebink, B.R., Dastani, M., Meyer, J.-J.C.: The OCC model revisited. In: Reichart, D. (ed.) Proceedings of the 4th Workshop on Emotion and Computing, KI 2009, Paderborn (2009)Google Scholar
  27. 27.
    Sun, R.: Learning, action and consciousness: a hybrid approach toward modelling consciousness. Neural Netw. 10(7), 1317–1331 (1997)CrossRefGoogle Scholar
  28. 28.
    Sun, R., Coward, L.A., Zenzen, M.J.: On levels of cognitive modeling. Philos. Psychol. 18(5), 613–637 (2005)CrossRefGoogle Scholar
  29. 29.
    Tambe, M., Johnson, W.L., Jones, R.M., Koss, F., Laird, J., Rosenbloom, P.S., Schwamb, K.: Intelligent agents for interactive simulation environments. AI Mag. 16(1), 15–39 (1995)Google Scholar
  30. 30.
    van Oijen, J., Dignum, F.: Towards a design approach for integrating BDI agents in virtual environments. In: Vilhjálmsson, H.H., Kopp, S., Marsella, S., Thórisson, K.R. (eds.) IVA 2011. LNCS, vol. 6895, pp. 462–463. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  31. 31.
    Wijermans, N., Jorna, R., Jager, W., van Vliet, T., Adang, O.: CROSS: modelling crowd behaviour with social-cognitive agents. J. Artif. Soc. Soc. Simul. 16(4), 1 (2013)Google Scholar
  32. 32.
    Wooldridge, M.: An Introduction to Multiagent Systems, 2nd edn. Wiley, Chichester (2009)Google Scholar
  33. 33.
    Wray, R.E., Laird, J.E., Nuxoll, A., Stokes, D., Kerfoot, A.: Synthetic adversaries for urban combat training. In: Proceedings of the 16th Conference on Innovative Applications of Artifical Intelligence, IAAI 2004, pp. 923–930. AAAI Press (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Science and TechnologyÖrebro UniversityÖrebroSweden

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