Search, Abstractions and Learning in Real-Time Strategy Games

A Dissertation Summary
  • Nicolas A. BarrigaEmail author
Dissertation and Habilitation Abstracts


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.


Real-time strategy games Game tree search Deep convolutional neural networks Evolutionary algorithms 


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Copyright information

© Gesellschaft für Informatik e.V. and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Universidad de TalcaTalcaChile

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