Abstract
Interaction between robotic systems and humans becomes increasingly important in industry, the private and the public sector. A robot which plays pool against a human opponent involves challenges most human robot interaction scenarios have in common: planning in a hybrid state space, numerous uncertainties and a human counterpart with a different visual perception system. In most situations it is important that the robot predicts human decisions to react appropriately. In the following, an approach to model and counter the behavior of human pool players is described. The resulting model allows to predict the stroke a human chooses to perform as well as the outcome of that stroke. This model is combined with a probabilistic search algorithm and implemented to an anthropomorphic robot. By means of this approach the robot is able to defeat a player with better manipulation skills. Furthermore it is outlined how this approach can be applied to other non-deterministic games or to tasks in a continuous state space.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Demiris, Y.: Prediction of intent in robotics and multi-agent systems. Cognitive Processing 8(3), 151–158 (2007)
Sebanz, N., Knoblich, G.: Prediction in joint action: What, when, and where. Topics in Cognitive Science 1(2), 353–367 (2009)
Wang, Z., Deisenroth, M., Amor, H.B., Vogt, D., Scholkopf, B., Peters, J.: Probabilistic modeling of human movements for intention inference. In: RSS (2012)
Howard, N., U. S. D. of Defense: Intention Awareness: In Command, Control, Communications, and Intelligence (C3I). University of Oxford (2000)
Smith, M.: Running the table: An AI for computer billiards. In: 21st National Conference on Artificial Intelligence (2006)
Archibald, C., Altman, A., Shoham, Y.: Analysis of a winning computational billiards player. In: International Joint Conferences on Artificial Intelligence (2009)
Landry, J.F., Dussault, J.P.: AI optimization of a billiard player. Journal of Intelligent & Robotic Systems 50, 399–417 (2007)
Dussault, J.-P., Landry, J.-F.: Optimization of a billiard player – tactical play. In: van den Herik, H.J., Ciancarini, P., Donkers, H.H.L.M(J.) (eds.) CG 2006. LNCS, vol. 4630, pp. 256–270. Springer, Heidelberg (2007)
Lin, Z., Yang, J., Yang, C.: Grey decision-making for a billiard robot. In: IEEE International Conference on Systems, Man and Cybernetics (2004)
Nierhoff, T., Heunisch, K., Hirche, S.: Strategic play for a pool-playing robot. In: IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO (2012)
Leckie, W., Greenspan, M.: An event-based pool physics simulator. In: van den Herik, H.J., Hsu, S.-C., Hsu, T.-s., Donkers, H.H.L.M(J.) (eds.) CG 2005. LNCS, vol. 4250, pp. 247–262. Springer, Heidelberg (2006)
Nierhoff, T., Kourakos, O., Hirche, S.: Playing pool with a dual-armed robot. In: IEEE International Conference on Robotics and Automation, ICRA (2011)
Alian, M., Shouraki, S.: A fuzzy pool player robot with learning ability. WSEAS Transactions on Electronics 1, 422–425 (2004)
Sutton, R., Barto, A.: Reinforcement learning: An introduction. Cambridge Univ. Press (1998)
Smith, M.: Pickpocket: A computer billiards shark. In: Artificial Intelligence (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Leibrandt, K., Lorenz, T., Nierhoff, T., Hirche, S. (2013). Modelling Human Gameplay at Pool and Countering It with an Anthropomorphic Robot. In: Herrmann, G., Pearson, M.J., Lenz, A., Bremner, P., Spiers, A., Leonards, U. (eds) Social Robotics. ICSR 2013. Lecture Notes in Computer Science(), vol 8239. Springer, Cham. https://doi.org/10.1007/978-3-319-02675-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-02675-6_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02674-9
Online ISBN: 978-3-319-02675-6
eBook Packages: Computer ScienceComputer Science (R0)