A Software Framework for Multi Player Robot Games

  • Søren Tranberg Hansen
  • Santiago Ontañón
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6414)

Abstract

Robot games have been proposed as a way to motivate people to do physical exercises while playing. Although this area is very new, both commercial and scientific robot games have been developed mainly based on interaction with a single user and a robot. The goal of this paper is to describe a generic software framework which can be used to create games where multiple players can play against a mobile robot. The paper shows how an adaptive AI system (D2) developed for real-time strategy (RTS) computer games can be successfully applied in a robotics context using the robotics control framework Player/Stage. D2 is based on Case-Based Planning which learns from demonstration. Using the proposed framework, the paper shows how a robot learns a strategy for an implementation of a simple game.

Keywords

Human Robot Interaction Games Artificial Intelligence 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aha, D., Molineaux, M., Ponsen, M.: Learning to win: Case-based plan selection in a real-time strategy game. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, pp. 5–20. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    A. Alzheimer’s Disease International. The prevalence of dementia worldwide. Technical report, Alzheimer’s Disease International, The International Federation of Alzheimer’s Disease and Related Disorders Societies, Inc. (2008)Google Scholar
  3. 3.
    Brooks, A.G., Gray, J., Hoffman, G.: Robot’s play: Interactive games with sociable machines. Computers in Entertainment 2, 10–10 (2004)CrossRefGoogle Scholar
  4. 4.
    Brown, R., Sugarman, H., Burstin, A.: Use of the nintendo wii fit for the treatment of balance problems in an elderly patient with stroke: A case report. In: International Journal of Rehabilitation Research, Proceedings of the 10th Congress of the European Federation for Research in Rehabilitation, vol. 32, p. 109 (2009)Google Scholar
  5. 5.
    Buro, M.: Real-time strategy games: A new ai research challenge. In: IJCAI 2003, pp. 1534–1535. Morgan Kaufmann, San Francisco (2003)Google Scholar
  6. 6.
    Csikszentmihalyi, M.: Beyond boredom and anxiety. Jossey-Bass Publishers (1975)Google Scholar
  7. 7.
    Dautenhahn, K.: Methodology & themes of human-robot interaction: A growing research field. International Journal of Advanced Robotic Systems 4(1), 103–108 (2007)Google Scholar
  8. 8.
    Fogg, B.: Persuasive Technology. Using Computers to Change What We Think and Do. Morgan Kaufmann, San Francisco (2003)Google Scholar
  9. 9.
    Fogg, B.J.: Captology. the study of computers as persuasive technologies. In: Proceedings of the CHI 1997, Extended abstracts on Human factors in computing systems, p. 129. AMC Press, New York (1997)Google Scholar
  10. 10.
    Fox, D.K.R.: The influence of physical activity on mental well being. Public Health Nutrition 2, 411 (1999)CrossRefGoogle Scholar
  11. 11.
    Gates, B.: A robot in every home. Scientific American 296(1), 58–65 (2007)CrossRefGoogle Scholar
  12. 12.
    Heerink, M., Krose, B., Evers, V., Wielinga, B.: Observing conversational expressiveness of elderly users interacting with a robot and screen. In: IEEE 10th International Conference on Rehabilitation Robotics, ICORR 2007, June 13-15, pp. 751–756 (2007)Google Scholar
  13. 13.
    Heerink, M., Krose, B., Wielinga, B., Evers, V.: Enjoyment intention to use and actual use of a conversational robot by elderly people. In: ACM/IEEE International Conference on Human-Robot Interaction archive Proceedings of the 3rd ACM/IEEE international conference on Human Robot Interaction, pp. 113–120 (2008)Google Scholar
  14. 14.
    IJsselsteijn, W., Nap, H.H., de Kort, Y., Poels, K.: Digital game design for elderly users. In: Proceedings of the 2007 Conference on Future Play, Future Play 2007, Toronto, Canada, November 14 - 17, pp. 17–22 (2007)Google Scholar
  15. 15.
    Kidd, C., Breazeal, C.: Sociable robot systems for real-world problems. In: IEEE International Workshop on Robot and Human Interactive Communication, ROMAN 2005, pp. 353–358 (2005)Google Scholar
  16. 16.
    Leite, I., Pereira, A., Martinho, C., Paiva, A.: Are emotional robots more fun to play with? In: The 17th IEEE International Symposium on Robot and Human Interactive Communication, ROMAN 2008, pp. 77–82 (2008)Google Scholar
  17. 17.
    Mehta, M., Ram, A.: Runtime behavior adaptation for real time interactive games. IEEE Transactions On Computational Intelligence And AI In GamesGoogle Scholar
  18. 18.
    Neufeldt, C.: Wii play with elderly people. International Reports on Socio-Informatics 6 (2009)Google Scholar
  19. 19.
    Ontañón, S., Bonnette, K., Mahindrakar, P., Gómez-Martín, M., Long, K., Radhakrishnan, J., Shah, R., Ram, A.: Learning from human demonstrations for real-time case-based planning. In: IJCAI 2009 Workshop on Learning Structural Knowledge From Observations, STRUCK 2009 (2009)Google Scholar
  20. 20.
    Ontañón, S., Mishra, K., Sugandh, N., Ram, A.: On-line case-based planning. Computational Intelligence Journal 26(1), 84–119 (2010)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Reynolds, C.W.: Competition, coevolution and the game of tag. In: Artificial Life IV (1994)Google Scholar
  22. 22.
    Svenstrup, M., Hansen, S.T., Andersen, H.J., Bak, T.: Pose estimation and adaptive robot behaviour for human-robot interaction. In: Proceedings of the 2009 IEEE International Conference on Robotics and Automation, ICRA 2009, Kobe, Japan (May 2009)Google Scholar
  23. 23.
    Tranberg, S., Svenstrup, M.: An adaptive robot game. In: ISR 2010 (2010)Google Scholar
  24. 24.
    Wendel-Vos, G., Schuit, A., Feskens, E., Boshuizen, H., Verschuren, W., Saris, W., Kromhout, D.: Physical activity and stroke. a meta-analysis of observational data. International Journal of Epidemiol. 33(4), 787–798 (2004)CrossRefGoogle Scholar
  25. 25.
    Xavier, J., Pacheco, M., Castro, D., Ruano, A., Nunes, U.: Fast line, arc/circle and leg detection from laser scan data in a player driver. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, ICRA 2005, April 18-22, pp. 3930–3935 (2005)Google Scholar
  26. 26.
    Zlotnik, H. (ed.): World Population Prospects - The 2004 Revision, Highlights (2005), United Nations, Population Division/DESA at www.unpopulation.org

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Søren Tranberg Hansen
    • 1
  • Santiago Ontañón
    • 2
  1. 1.Danish Technological InstituteCenter For Robot TechnologyOdense MDenmark
  2. 2.IIIA, Artificial Intelligence Research InstituteCSIC, Spanish Council for Scientific ResearchBellaterraSpain

Personalised recommendations