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on autonomous agents and multiagent systems, pp 21–28
16. Young J, Hawes N (2012) Evolutionary learning of goal priorities in a real-time strategy
game. In: AIIDE-12
17. Sánchez PG, Tonda AP, Garcia AM et al (2015) Towards automatic StarCraft strategy
generation using genetic programming. In: IEEE CIG, pp 284–291
18. Köstler H, Gmeiner B (2013) A multi-objective genetic algorithm for build order
optimization in StarCraft II. KI 27:221–233
19. Carlos A, Gary B, David A (2007) Evolutionary algorithms for solving multi-objective
problems. Springer
20. Justesen N, Risi S (2017) Continual online evolutionary planning for in game build order
adaptation in StarCraft. In: GECCO, pp 187–194
21. Svendsen JB, Rathe EA (2012) Micromanagement in StarCraft using potential fields tuned
with a multi-objective genetic algorithm. Master’s thesis, NTNU
22. Gabriel I, Negru V, Zaharie D (2012) Neuroevolution based multi-agent system for
micromanagement in real-time strategy games. In: BCI, pp 32–39
23. Santiago O, Synnaeve G, Uriarte A et al (2015) RTS AI problems and techniques. https://
doi.org/10.1007/978-3-319-08234-9_17-1
24. Lu F, Yamamoto K, Nomura LH et al (2013) Fighting game artificial intelligence
competition platform. In: IEEE GCCE. https://doi.org/10.1109/gcce.2013.6664844
25. Cho HC, Kim KJ, Cho SB (2013) Replay-based strategy prediction and build order
adaptation for StarCraft AI bots. In: IEEE CIG, pp 1–7
26. Dereszynski E, Hostetler J, Fern A (2011) Learning probabilistic behavior models in realtime strategy games. In: AIIDE, pp 20–25
27. Justesen N, Risi S (2017) Learning macromanagement in StarCraft from replays using deep
learning. In: IEEE CIG, pp 162–169
28. Zhao D, Shao K, Zhu Y et al (2016) Review of deep reinforcement learning and discussions
on the development of computer Go. IET Control Theory A 33(6):701–717
29. Usunier N, Synnaeve G, Lin Z et al (2017) Episodic exploration for deep deterministic
policies: an application to StarCraft micromanagement tasks. In: International conference on
learning representations
30. Shao K, Zhu Y, Zhao D (2018) StarCraft micromanagement with reinforcement learning and
curriculum transfer learning. In: IEEE TETCI. https://doi.org/10.1109/tetci.2018.2823329
31. Bloembergen D, Tuyls K, Hennes D et al (2015) Evolutionary dynamics of multi-agent
learning: a survey. J Artif Intell Res 53(1):659–697
32. Al-Yaseen WL, Othman ZA, Nazri MZA (2015) Hybrid modified k-means with C4.5 for
intrusion detection systems in multiagent systems. Sci World J. https://doi.org/10.1155/
2015/294761
33. Zaki MJ (2001) SPADE: an efficient algorithm for mining frequent sequences. Mach Learn
42(1–2):31–60
A Review of Artificial Intelligence for Games
303