Abstract
Basically, in (one-player) war Real Time Strategy (wRTS) games a human player controls, in real time, an army consisting of a number of soldiers and her aim is to destroy the opponent’s assets where the opponent is a virtual (i.e., non-human player controlled) player that usually consists of a pre-programmed decision-making script. These scripts have usually associated some well-known problems (e.g., predictability, non-rationality, repetitive behaviors, and sensation of artificial stupidity among others). This paper describes a method for the automatic generation of virtual players that adapt to the player skills; this is done by building initially a model of the player behavior in real time during the game, and further evolving the virtual player via this model in-between two games. The paper also shows preliminary results obtained on a one-player wRTS game constructed specifically for experimentation.
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Lidén, L.: Artificial stupidity: The art of intentional mistakes. In: AI Game Programming Wisdom 2, pp. 41–48. Charles River Media, Inc. (2004)
Ahlquist, J.B., Novak, J.: Game Artificial Intelligence. Game Development essentials. Thomson Delmar Learning, Canada (2008)
Buro, M.: Call for AI research in RTS games. In: Fu, D., Orkin, J. (eds.) AAAI workshop on Challenges in Game AI, San Jose, pp. 139–141 (2004)
Corruble, V., Madeira, C.A.G., Ramalho, G.: Steps toward building of a good ai for complex wargame-type simulation games. In: Mehdi, Q.H., Gough, N.E. (eds.) 3rd International Conference on Intelligent Games and Simulation (GAME-ON 2002), London, UK (2002)
Forbus, K.D., Mahoney, J.V., Dill, K.: How qualitative spatial reasoning can improve strategy game ais. IEEE Intelligent Systems 17(4), 25–30 (2002)
Louis, S.J., Miles, C.: Playing to learn: case-injected genetic algorithms for learning to play computer games. IEEE Trans. Evol. Comput. 9(6), 669–681 (2005)
Stanley, K.O., Bryant, B.D., Miikkulainen, R.: Real-time neuroevolution in the nero video game. IEEE Trans. Evol. Comput. 9(6), 653–668 (2005)
Livingstone, D.: Coevolution in hierarchical ai for strategy games. In: IEEE Symposium on Computational Intelligence and Games (CIG 2005), Essex, UK, IEEE, Los Alamitos (2005)
Miles, C., Louis, S.J.: Co-evolving real-time strategy game playing influence map trees with genetic algorithms. In:International Congress on Evolutionary Computation, Portland, Oregon. IEEE press, New York (2006)
Lichocki, P., Krawiec, K., Jaśkowski, W.: Evolving teams of cooperating agents for real-time strategy game. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., Machado, P. (eds.) EvoWorkshops 2009. LNCS, vol. 5484, pp. 333–342. Springer, Heidelberg (2009)
Beume, N.,et al.: Intelligent anti-grouping in real-time strategy games. In: International Symposium on Computational Intelligence in Games, Perth, Australia, pp. 63–70 (2008)
Keaveney, D., O’Riordan, 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 multi-agent potential field-based bot for a full RTS game scenario. In: Darken, C., Youngblood, G.M. (eds.) Proc. Fifth Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE 2009), Stanford, California, USA, The AAAI Press, Menlo Park (2009)
Sweetser, P.: Emergence in Games. Game development. Charles River Media, Boston (2008)
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García Gutiérrez, J.A., Cotta, C., Fernández Leiva, A.J. (2011). Design of Emergent and Adaptive Virtual Players in a War RTS Game. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) Foundations on Natural and Artificial Computation. IWINAC 2011. Lecture Notes in Computer Science, vol 6686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21344-1_39
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DOI: https://doi.org/10.1007/978-3-642-21344-1_39
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