Abstract
Due to the manifold challenges that arise when developing an artificial intelligence that can compete with human players, the popular realtime-strategy game Starcraft: Broodwar (BW) has received attention from the computational intelligence research community. It is an ideal testbed for methods for self-adaption at runtime designed to work in complex technical systems. In this work, we utilize the broadlys-used Extended Classifier System (XCS) as a basis to develop different models of BW micro AIs: the Defender, the Attacker, the Explorer and the Strategist. We evaluate theses AIs with a focus on their adaptive and co-evolutionary behaviors. To this end, we stage and analyze the outcomes of a tournament among the proposed AIs and we also test them against a non-adaptive player to provide a proper baseline for comparison and learning evolution. Of the proposed AIs, we found the Explorer to be the best performing design, but, also that the Strategist shows an interesting behavioral evolution.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Starcraft and Starcraft: Broodwar are trademarks of Blizzard Entertainment.
- 2.
- 3.
Starcraft Micro AI Tournament.
References
Müller-Schloer, C., Schmeck, H.: Organic computing - quo vadis? In: Müller-Schloer, C., Schmeck, H., Ungerer, T. (eds.) Organic Computing - A Paradigm Shift for Complex Systems, pp. 615–625. Birkhäuser, Verlag (2011)
Ontañón, S., Synnaeve, G., Uriarte, A., Richoux, F., Churchill, D., Preuss, M.: A survey of real-time strategy game AI research and competition in StarCraft. IEEE Trans. Comput. Intell. AI Games 5(4), 293–311 (2013)
Synnaeve, G., Bessière, P.: A Bayesian model for opening prediction in RTS games with application to StarCraft. In: Cho, S.B., Lucas, S.M., Hingston, P. (eds.) CIG, pp. 281–288. IEEE (2011)
Weber, B.G., Mateas, M., Jhala, A.: Applying goal-driven autonomy to StarCraft. In: Youngblood, G.M., Bulitko, V. (eds.) AIIDE. The AAAI Press (2010)
Shoham, Y.: Agent-oriented programming. Artif. Intell. 60(1), 51–92 (1993)
Blackadar, M., Denzinger, J.: Behavior learning-based testing of StarCraft competition entries. In: Bulitko, V., Riedl, M.O. (eds.) AIIDE. The AAAI Press (2011)
Robertson, G., Watson, I.: An improved dataset and extraction process for StarCraft AI. In: The Twenty-Seventh International Flairs Conference (2014)
Weber, B.G., Ontañón, S.: Using automated replay annotation for case-based planning in games. In: ICCBR Workshop on CBR for Computer Games (ICCBR-Games) (2010)
Churchill, D., Buro, M.: Build order optimization in StarCraft. In: Bulitko, V., Riedl, M.O. (eds.) AIIDE. The AAAI Press (2011)
Hagelback, J.: Potential-field based navigation in StarCraft. In: IEEE Conference on Computational Intelligence and Games (CIG), Sep 2012, pp. 388–393 (2012)
Yi, S.: Adaptive strategy decision mechanism for StarCraft AI. In: Han, M.-W., Lee, J. (eds.) EKC 2010. SPPHY, vol. 138, pp. 47–57. Springer, Heidelberg (2011)
Synnaeve, G., Bessière, P.: A Bayesian tactician. In: Proceedings of the Computer Games Workshop at the European Conference of Artificial Intelligence 2012, pp. 114–125 (2012)
Garcia-Sanchez, P., Tonda, A., Mora, A., Squillero, G., Merelo, J.: Towards automatic StarCraft strategy generation using genetic programming. In: 2015 IEEE Conference on Computational Intelligence and Games (CIG), pp. 284–291, August 2015
Parra, R., Garrido, L.: Bayesian networks for micromanagement decision imitation in the RTS game StarCraft. In: Batyrshin, I., Mendoza, M.G. (eds.) MICAI 2012, Part II. LNCS, vol. 7630, pp. 433–443. Springer, Heidelberg (2013)
Wender, S., Watson, I.D.: Applying reinforcement learning to small scale combat in the real-time strategy game StarCraft: Broodwar. In: CIG, pp. 402–408. IEEE (2012)
Holland, J.H.: Adaptation*. In: Rosen, R., Snell, F.M. (eds.) Progress in Theoretical Biology, pp. 263–293. Academic Press, New York (1976)
Holland, J.H., Reitman, J.S.: Cognitive systems based on adaptive algorithms. SIGART Bull. 63, 49–49 (1977)
Wilson, S.W.: Classifier fitness based on accuracy. Evol. Comput. 3(2), 149–175 (1995)
Reynolds, C.W.: Flocks, herds, and schools: a distributed behavioral model. Comput. Graph. 21(4), 25–34 (1987)
Lin, C.S., Ting, C.K.: Emergent tactical formation using genetic algorithm in real-time strategy games. In: Proceedings of the 2011 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011, Computer Society, pp. 325–330. IEEE, Washington (2011)
Rudolph, S., Tomforde, S., Sick, B., Hähner, J.: A mutual influence detection algorithm for systems with local performance measurement. In: Proceedings of the 9th IEEE International Conference on Self-adapting and Self-organising Systems (SASO15), held September 21st to September 25th in Boston, USA, pp. 144–150 (2015)
Fisch, D., Jänicke, M., Sick, B., Müller-Schloer, C.: Quantitative emergence - a refined approach based on divergence measures. In: 4th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO), Sep 2010, pp. 94–103 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Rudolph, S., von Mammen, S., Jungbluth, J., Hähner, J. (2016). Design and Evaluation of an Extended Learning Classifier-Based StarCraft Micro AI. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. https://doi.org/10.1007/978-3-319-31204-0_43
Download citation
DOI: https://doi.org/10.1007/978-3-319-31204-0_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31203-3
Online ISBN: 978-3-319-31204-0
eBook Packages: Computer ScienceComputer Science (R0)