Abstract
Behaviour trees provide the possibility of improving on existing Artificial Intelligence techniques in games by being simple to implement, scalable, able to handle the complexity of games, and modular to improve reusability. This ultimately improves the development process for designing automated game players. We cover here the use of behaviour trees to design and develop an AI-controlled player for the commercial real-time strategy game DEFCON. In particular, we evolved behaviour trees to develop a competitive player which was able to outperform the game’s original AI-bot more than 50% of the time. We aim to highlight the potential for evolving behaviour trees as a practical approach to developing AI-bots in games.
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.
Similar content being viewed by others
References
Bauckhage, C., Thurau, C.: Exploiting the fascination: Video games in machine learning research and education. In: Proceedings of the 2nd International Workshop in Computer Game Design and Technology (2004)
Baumgarten, R., Colton, S., Morris, M.: Combining AI Methods for Learning Bots in a Real-Time Strategy Game. Int. J. of Computer Games Tech. (2009)
Bryson, J.: Action selection and individuation in agent based modelling. In: Proceedings of the Argonne National Laboratories Agent Conference (2003)
Bryson, J.: The behavior-oriented design of modular agent intelligence. In: Kowalczyk, R., Müller, J.P., Tianfield, H., Unland, R. (eds.) NODe-WS 2002. LNCS (LNAI), vol. 2592, pp. 61–76. Springer, Heidelberg (2003)
Hagelbäck, J., Johansson, S.: Using multi-agent potential fields in real-time strategy games. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multi-agent Systems, vol. 2 (2008)
Hauptman, A., Sipper, M.: GP-endchess: Using genetic programming to evolve chess endgame players. In: Keijzer, M., Tettamanzi, A.G.B., Collet, P., van Hemert, J., Tomassini, M. (eds.) EuroGP 2005. LNCS, vol. 3447, pp. 120–131. Springer, Heidelberg (2005)
Hecker, C., McHugh, L., Argenton, M., Dyckhoff, M.: Three Approaches to Halo-style Behavior Tree AI. In: Games Developer Conference, Audio Talk (2007)
Isla, D.: Managing complexity in the Halo 2 AI system. In: Proceedings of the Game Developers Conference (2005)
Langdon, W.: Size fair and homologous tree genetic programming crossovers. Genetic programming and evolvable machines 1(1/2), 95–119 (2000)
Luke, S.: Genetic programming produced competitive soccer softbot teams for RoboCup. In: Proceedings of the 3rd Annual Conference of Genetic Programming (1998)
Orkin, J.: Three states and a plan: the AI of FEAR. In: Proceedings of the Game Developers Conference (2006)
Priesterjahn, S., Kramer, O., Weimer, A., Goebels, A.: Evolution of human-competitive agents in modern computer games. In: Proceedings of the IEEE Congress on Evolutionary Computation (2006)
Togelius, J., De Nardi, R., Lucas, S.: Towards automatic personalised content creation for racing games. In: Proceedings of the IEEE Symposium on Computational Intelligence and Games (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lim, CU., Baumgarten, R., Colton, S. (2010). Evolving Behaviour Trees for the Commercial Game DEFCON. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2010. Lecture Notes in Computer Science, vol 6024. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12239-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-12239-2_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12238-5
Online ISBN: 978-3-642-12239-2
eBook Packages: Computer ScienceComputer Science (R0)