Barriga, Nicolas A.2021-04-232021-04-2320202020http://dx.doi.org/10.1007/s13218-019-00614-0https://dl.gi.de/handle/20.500.12116/36275Real-Time Strategy Games’ large state and action spaces pose a significant hurdle to traditional AI techniques. We propose decomposing the game into sub-problems and integrating the partial solutions into action scripts that can be used as abstract actions by a search or machine learning algorithm. The resulting high level algorithm produces sound strategic choices, and can then be combined with a low-level search algorithm to refine tactical choices. We show strong results in SparCraft, Starcraft: Brood War and $$\mu $$ μ RTS against state-of-the-art agents. We expect advances in RTS AI can be used in commercial videogames for playtesting and game balancing, while also having possible real-world applications.Deep convolutional neural networksEvolutionary algorithmsGame tree searchReal-time strategy gamesText/Journal Article10.1007/s13218-019-00614-01610-1987