Abstract
This paper proposes an Evolutionary Algorithm for fine-tuning the behavior of a bot designed for playing Planet Wars, a game that has been selected for the the Google Artificial Intelligence Challenge 2010. The behavior engine of the proposed bot is based on a set of rules established by means of heuristic experimentation, followed by the application of an evolutionary algorithm to set the constants, weights and probabilities needed by those rules. This bot eventually defeated the baseline bot used to design it in most maps, and eventually played in the Google AI competition, obtaining a ranking in the top 20%.
Supported in part by Andalusian Government grant P08-TIC-03903, by the CEI BioTIC GENIL (CEB09-0010) Programa CEI del MICINN (PYR-2010-13) project, the Junta de Andalucía TIC-3903 and P08-TIC-03928 projects, and the Portuguese Fellowship SFRH /BPD / 66876 / 2009.
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References
Sweetser, P.: Emergence in Games. In: Game development, Charles River Media, Boston (2008)
Ontañón, S., Mishra, K., Sugandh, N., Ram, A.: Case-based planning and execution for real-time strategy games. In: Weber, R.O., Richter, M.M. (eds.) ICCBR 2007. LNCS (LNAI), vol. 4626, pp. 164–178. Springer, Heidelberg (2007)
Keaveney, D., OŔiordan, C.: Evolving robust strategies for an abstract real-time strategy game. In: International Symposium on Computational Intelligence in Games, Milano. Italy, pp. 371–378. IEEE Press, New York (2009)
Hagelbäck, J., Johansson, S.J.: A multiagent potential field-based bot for real-time strategy games. Int. J. Comput. Games Technol., 2009, 4:1–4:10 (2009)
Livingstone, D.: Coevolution in hierarchical ai for strategy games. In: IEEE Symposium on Computational Intelligence and Games (CIG 2005), pp. 190–194. IEEE, Colchester (2005)
Avery, P., Louis, S.: Coevolving team tactics for a real-time strategy game. In: Proceedings of the 2010 IEEE Congress on Evolutionary Computation (2010)
Ponsen, M., Munoz-Avila, H., Spronck, P., Aha, D.W.: Automatically generating game tactics through evolutionary learning. AI Magazine 27(3), 75–84 (2006)
Jang, S.H., Yoon, J.W., Cho, S.B.: Optimal strategy selection of non-player character on real time strategy game using a speciated evolutionary algorithm. In: Proceedings of the 5th IEEE Symposium on Computational Intelligence and Games (CIG 2009), pp. 75–79. IEEE Press, Piscataway (2009)
Lucas, S.: Computational intelligence and games: Challenges and opportunities. International Journal of Automation and Computing 5(1), 45–57 (2008)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1996)
Herrera, F., Lozano, M., Sánchez, A.M.: A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study. International Journal of Intelligent Systems 18, 309–338 (2003)
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Fernández-Ares, A., Mora, A.M., Merelo, J.J., García-Sánchez, P., Fernandes, C.M. (2011). Optimizing Strategy Parameters in a Game Bot. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21498-1_41
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DOI: https://doi.org/10.1007/978-3-642-21498-1_41
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