Abstract
Real-Time Strategy (RTS) games have become an attractive domain for AI research in recent years, due to their dynamic, multi-agent and multi-objective environments. Micromanagement, a core component of many RTS games, involves the control of multiple agents to accomplish goals that require fast, real time assessment and reaction. In this paper, we present the application and evaluation of a Neuroevolution technique in evolving micromanagement agents for the RTS game Starcraft: Brood War (SC:BW). The NeuroEvolution of Augmented Topologies (NEAT) algorithm, both in its standard form and its real-time variant (rtNEAT) is comparatively evaluated in micromanagement tasks. Preliminary results suggest the general viability of these techniques in comparison to traditional, non-adaptive AI. Further analysis of each algorithm identified differences in task performance and learning rate.
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
Laird, J., VanLent, M.: Human-level AI’s killer application: Interactive computer games. AI Magazine 22(2), 15–26 (2001)
Buro, M.: Call for AI research in RTS games. In: Proceedings of the AAAI 2004 Workshop on Challenges in Game AI, pp. 2–4 (2004)
Siwek, S.E.: Video Games in the 21st Century. Technical report. Entertainment Software Association (2010)
Yildirim, S., Stene, S.B.: A survey on the need and use of ai in game agents. In: Proceedings of the 2008 Spring Simulation Multiconference, pp. 124–131 (2008)
Mehta, M., Ontañón, S., Amundsen, T., Ram, A.: Authoring behaviors for games using learning from demonstration. In: Workshop on Case-Based Reasoning for Computer Games, ICCBR (2009)
Olesen, J.K., Yannakakis, G.N., Hallam, J.: Real-time challenge balance in an RTS game using rtNEAT. In: 2008 IEEE Symposium on Computational Intelligence and Games, pp. 87–94 (2008)
Buro, M., Furtak, T.M.: RTS games and real-time AI research. In: Proceedings of the Behavior Representation in Modeling and Simulation Conference, pp. 63–70 (2004)
Stanley, K.O., Miikkulainen, R.: Efficient Evolution of Neural Network Topologies. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002). IEEE (2002)
Wender, S., Watson, I.: Applying reinforcement learning to small scale combat in the real-time strategy game StarCraft:Broodwar. In: Computational Intelligence and Games (CIG), pp. 402–408 (2012)
Shantia, A., Begue, E., Wiering, M.: Connectionist reinforcement learning for intelligent unit micro management in starcraft. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 1794–1801 (2011)
Cadena, P., Garrido, L.: Fuzzy Case-Based Reasoning for Managing Strategic and Tactical Reasoning in StarCraft. In: Batyrshin, I., Sidorov, G. (eds.) MICAI 2011, Part I. LNCS, vol. 7094, pp. 113–124. Springer, Heidelberg (2011)
Weber, B., Mateas, M., Jhala, A.: Applying goal-driven autonomy to StarCraft. In: Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2010 (2010)
Davis, I.L.: Strategies for strategy game AI. In: Proceedings of the AAAI Spring Symposium on Artificial Intelligence and Computer Games, pp. 24–27 (1999)
Gabriel, I., Negru, V., Zaharie, D.: Neuroevolution based multi-agent system for micromanagement in real-time strategy games. In: Proceedings of the Fifth Balkan Conference in Informatics - BCI 2012, p. 32 (2012)
Yao, X.: Evolving artificial neural networks. Proceedings of the IEEE 87, 1423–1447 (1999)
Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99–127 (2002)
Stanley, K.O.: Evolving neural network agents in the NERO video game. In: Proceedings of the IEEE 2005 Symposium on Computational Intelligence and Games, pp. 182–189 (2005)
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 International Conference on Computational Intelligence and Games, pp. 75–79 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhen, J.S., Watson, I. (2013). Neuroevolution for Micromanagement in the Real-Time Strategy Game Starcraft: Brood War. In: Cranefield, S., Nayak, A. (eds) AI 2013: Advances in Artificial Intelligence. AI 2013. Lecture Notes in Computer Science(), vol 8272. Springer, Cham. https://doi.org/10.1007/978-3-319-03680-9_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-03680-9_28
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03679-3
Online ISBN: 978-3-319-03680-9
eBook Packages: Computer ScienceComputer Science (R0)