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.
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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
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DOI: https://doi.org/10.1007/978-3-319-03680-9_28
Publisher Name: Springer, Cham
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