Search, Abstractions and Learning in Real-Time Strategy Games

Abstract

Real-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.