Abstract
In this paper, a new approach to automatically generating game strategies based on the game conditions is presented. A game policy is defined and applied by a human coach who establishes the attitude of the team for defending or attacking. A simple neural net model is applied using current and previous game experience to classify the game’s parameters so that the new game conditions can be determined so that a robotic team can modify its strategy on the fly. Results of the implemented model for a robotic soccer team are discussed.
This research is partially sponsored by the National Council for Scientific and Technological Research (FONDECYT, Chile) under grant number 1070714 “An Interactive Natural-Language Dialogue Model for Intelligent Filtering based on Patterns Discovered from Text Documents”.
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d’nverno, M., Luck, M.: Understanding Agent Systems, 2nd edn. Springer, Heidelberg (2004)
Frias, V., Sklar, E., Parsons, S.: Exploring auction mechanisms for role assignment in teams of autonomous robots. In: Nardi, D., Riedmiller, M., Sammut, C., Santos-Victor, J. (eds.) RoboCup 2004. LNCS (LNAI), vol. 3276, pp. 532–539. Springer, Heidelberg (2005)
Gerkey, B., Mataric, J.: A formal analysis and taxonomy of task allocation in multi-robot systems. The International Journal of Robotics Research 23(9), 939–954 (2004)
Hagan, M., Demuth, H., Beale, M.: Neural Network Design. Martin Hagan (2002)
Kuhlmann, G., Knox, W., Stone, P.: Know thine enemy: A champion robocup coach agent. In: Twenty-First National Conference on Artifical Inteligence (AAAI 2006), Boston, MA, July 2006, AAAI Press (2006)
Lerman, K., Jones, C., Galstyan, A., Mataric, M.: Analysis of dynamic task allocation in multi-robot systems. The International Journal of Robotics Research 25(3), 225–241 (2006)
Mitchell, M.: An Introduction to Genetic Algorithms. The MIT Press (1996)
Quinlan, M.J., Nicklin, S.P., Hong, K., Henderson, N., King, R.: The 2005 nubots team report. Technical report, School of Electrical Engineering and Computer Science, The University of Newcastle, Australia (2006)
Riley, P., Veloso, M.: Planning for distributed execution through use of probabilistic opponent models. In: Proceedings of the Sixth International Conference on AI Planning and Scheduling, pp. 72–81 (2002)
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Atkinson, J., Rojas, D. (2008). Generating Dynamic Formation Strategies Based on Human Experience and Game Conditions. In: Visser, U., Ribeiro, F., Ohashi, T., Dellaert, F. (eds) RoboCup 2007: Robot Soccer World Cup XI. RoboCup 2007. Lecture Notes in Computer Science(), vol 5001. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68847-1_14
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DOI: https://doi.org/10.1007/978-3-540-68847-1_14
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