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Search, Abstractions and Learning in Real-Time Strategy Games

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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 \(\mu \)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.

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Authors and Affiliations

  1. Universidad de Talca, 2 Norte 685, Talca, Chile

    Nicolas A. Barriga

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  1. Nicolas A. Barriga
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Correspondence to Nicolas A. Barriga.

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Barriga, N.A. Search, Abstractions and Learning in Real-Time Strategy Games. Künstl Intell 34, 101–103 (2020). https://doi.org/10.1007/s13218-019-00614-0

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  • DOI: https://doi.org/10.1007/s13218-019-00614-0

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