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Neuroevolution for Micromanagement in the Real-Time Strategy Game Starcraft: Brood War

  • Jacky Shunjie Zhen
  • Ian Watson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8272)

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.

Keywords

Real-Time Strategy Games Neuroevolution Evolutionary Computation 

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References

  1. 1.
    Laird, J., VanLent, M.: Human-level AI’s killer application: Interactive computer games. AI Magazine 22(2), 15–26 (2001)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    Siwek, S.E.: Video Games in the 21st Century. Technical report. Entertainment Software Association (2010)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    Stanley, K.O., Miikkulainen, R.: Efficient Evolution of Neural Network Topologies. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002). IEEE (2002)Google Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Weber, B., Mateas, M., Jhala, A.: Applying goal-driven autonomy to StarCraft. In: Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2010 (2010)Google Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    Yao, X.: Evolving artificial neural networks. Proceedings of the IEEE 87, 1423–1447 (1999)CrossRefGoogle Scholar
  16. 16.
    Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99–127 (2002)CrossRefGoogle Scholar
  17. 17.
    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)Google Scholar
  18. 18.
    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)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Jacky Shunjie Zhen
    • 1
  • Ian Watson
    • 1
  1. 1.Department of Computer ScienceUniversity of AucklandNew Zealand

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