Abstract
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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References
Barbu, A., Narayanaswamy, S., Siskind, J.: Learning physically-instantiated game play through visual observation. In: Proc. of ICRA, pp. 1879–1886. IEEE Press (2010)
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)
Broekens, J., Heerink, M., Rosendal, H.: Assistive social robots in elderly care: a review. Gerontechnology 8(2) (2009)
Clune, J.: Heuristic evaluation functions for general game playing. In: Proc. of AAAI, pp. 1134–1139 (2007)
Genesereth, M., Love, N., Pell, B.: General game playing: Overview of the AAAI competition. AI Magazine 26(2), 62–72 (2005)
Goldfeder, C., Ciocarlie, M.T., Dang, H., Allen, P.K.: The columbia grasp database. In: Proc. of ICRA, pp. 1710–1716. IEEE Press (2009)
Haufe, S., Schiffel, S., Thielscher, M.: Automated verification of state sequence invariants in general game playing. Artificial Intelligence 187-188, 1–30 (2012)
Kaiser, Ł.: Learning games from videos guided by descriptive complexity. In: Proc. of AAAI, pp. 963–969 (2012)
Kemp, C.C., Edsinger, A., Torres-Jara, E.: Challenges for robot manipulation in human environments. IEEE Robotics & Automation Magazine 14(1), 20–29 (2007)
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)
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)
Schiffel, S., Thielscher, M.: Fluxplayer: A successful general game player. In: Proc. of AAAI, pp. 1191–1196 (2007)
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)
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Rajaratnam, D., Thielscher, M. (2013). Towards General Game-Playing Robots: Models, Architecture and Game Controller. In: Cranefield, S., Nayak, A. (eds) AI 2013: Advances in Artificial Intelligence. AI 2013. Lecture Notes in Computer Science(), vol 8272. Springer, Cham. https://doi.org/10.1007/978-3-319-03680-9_29
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DOI: https://doi.org/10.1007/978-3-319-03680-9_29
Publisher Name: Springer, Cham
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