Abstract
We apply gene expression programing to evolve a player for a real-time strategy (RTS) video game. The paper describes the game, evolutionary encoding of strategies and the technical implementation of experimental framework. In the experimental part, we compare two setups that differ with respect to the used approach of task decomposition. One of the setups turns out to be able to evolve an effective strategy, while the other leads to more sophisticated yet inferior solutions. We discuss both the quantitative results and the behavioral patterns observed in the evolved strategies.
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
Azaria, Y., Sipper, M.: GP-gammon: Genetically programming backgammon players. Genetic Programming and Evolvable Machines 6(3), 283–300 (2005); Published online: 12 August 2005
Buro, M.: Real-time strategy games: A new AI research challenge. In: Proceedings of the International Joint Conference on AI 2003, Acapulco, Mexico (2003)
Buro, M., Furtak, T.: ORTS open real time strategy framework (2005), http://www.cs.ualberta.ca/~mburo/orts/
Corno, F., Sanchez, E., Squillero, G.: On the evolution of corewar warriors. In: Proceedings of the 2004 IEEE Congress on Evolutionary Computation, Portland, Oregon, pp. 133–138. IEEE Press, Los Alamitos (2004)
Ferreira, C.: Gene expression programming: a new adaptive algorithm for solving problems. Complex Systems 13, 87 (2001)
Ferreira, C.: Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. Studies in Computational Intelligence. Springer, New York (2006)
Lichocki, P.: Evolving players for a real-time strategy game using gene expression programming. Master’s thesis, Poznan Universtity of Technology (2008)
Luke, S.: Genetic programming produced competitive soccer softbot teams for Robocup 1997. In: Koza, J.R., Banzhaf, W., Chellapilla, K., Deb, K., Dorigo, M., Fogel, D.B., Garzon, M.H., Goldberg, D.E., Iba, H., Riolo, R. (eds.) Genetic Programming 1998: Proceedings of the Third Annual Conference, University of Wisconsin, Madison, Wisconsin, USA, pp. 214–222. Morgan Kaufmann, San Francisco (1998)
Luke, S.: ECJ evolutionary computation system (2002), http://cs.gmu.edu/eclab/projects/ecj/
Pollack, J.B., Blair, A.D.: Co-evolution in the successful learning of backgammon strategy. Machine Learning 32(3), 225–240 (1998)
Sipper, M.: Attaining human–competitive game playing with genetic programming. In: El Yacoubi, S., Chopard, B., Bandini, S. (eds.) ACRI 2006. LNCS, vol. 4173, p. 13. Springer, Heidelberg (2006)
Stanley, K., Bryant, B., Miikkulainen, R.: Real-time neuroevolution in the NERO video game. IEEE Transactions on Evolutionary Computation 9(6), 653–668 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lichocki, P., Krawiec, K., Jaśkowski, W. (2009). Evolving Teams of Cooperating Agents for Real-Time Strategy Game. In: Giacobini, M., et al. Applications of Evolutionary Computing. EvoWorkshops 2009. Lecture Notes in Computer Science, vol 5484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01129-0_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-01129-0_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01128-3
Online ISBN: 978-3-642-01129-0
eBook Packages: Computer ScienceComputer Science (R0)