Towards General Game-Playing Robots: Models, Architecture and Game Controller

  • David Rajaratnam
  • Michael Thielscher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8272)


General Game Playing aims at AI systems that can understand the rules of new games and learn to play them effectively without human intervention. Our paper takes the first step towards general game-playing robots, which extend this capability to AI systems that play games in the real world. We develop a formal model for general games in physical environments and provide a systems architecture that allows the embedding of existing general game players as the “brain” and suitable robotic systems as the “body” of a general game-playing robot. We also report on an initial robot prototype that can understand the rules of arbitrary games and learns to play them in a fixed physical game environment.


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  1. 1.
    Barbu, A., Narayanaswamy, S., Siskind, J.: Learning physically-instantiated game play through visual observation. In: Proc. of ICRA, pp. 1879–1886. IEEE Press (2010)Google Scholar
  2. 2.
    Björnsson, Y., Finnsson, H.: CADIAPLAYER: A simulation-based general game player. IEEE Transactions on Computational Intelligence and AI in Games 1(1), 4–15 (2009)CrossRefGoogle Scholar
  3. 3.
    Broekens, J., Heerink, M., Rosendal, H.: Assistive social robots in elderly care: a review. Gerontechnology 8(2) (2009)Google Scholar
  4. 4.
    Clune, J.: Heuristic evaluation functions for general game playing. In: Proc. of AAAI, pp. 1134–1139 (2007)Google Scholar
  5. 5.
    Genesereth, M., Love, N., Pell, B.: General game playing: Overview of the AAAI competition. AI Magazine 26(2), 62–72 (2005)Google Scholar
  6. 6.
    Goldfeder, C., Ciocarlie, M.T., Dang, H., Allen, P.K.: The columbia grasp database. In: Proc. of ICRA, pp. 1710–1716. IEEE Press (2009)Google Scholar
  7. 7.
    Haufe, S., Schiffel, S., Thielscher, M.: Automated verification of state sequence invariants in general game playing. Artificial Intelligence 187-188, 1–30 (2012)Google Scholar
  8. 8.
    Kaiser, Ł.: Learning games from videos guided by descriptive complexity. In: Proc. of AAAI, pp. 963–969 (2012)Google Scholar
  9. 9.
    Kemp, C.C., Edsinger, A., Torres-Jara, E.: Challenges for robot manipulation in human environments. IEEE Robotics & Automation Magazine 14(1), 20–29 (2007)CrossRefGoogle Scholar
  10. 10.
    Lai, K., Bo, L., Ren, X., Fox, D.: A large-scale hierarchical multi-view rgb-d object dataset. In: Proc. of ICRA, pp. 1817–1824. IEEE Press (2011)Google Scholar
  11. 11.
    Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software (2009)Google Scholar
  12. 12.
    Schiffel, S., Thielscher, M.: Fluxplayer: A successful general game player. In: Proc. of AAAI, pp. 1191–1196 (2007)Google Scholar
  13. 13.
    Schiffel, S., Thielscher, M.: A Multiagent Semantics for the Game Description Language. In: Filipe, J., Fred, A., Sharp, B. (eds.) ICAART 2009. CCIS, vol. 67, pp. 44–55. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • David Rajaratnam
    • 1
  • Michael Thielscher
    • 1
  1. 1.The University of New South WalesSydneyAustralia

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