Game-Tree Search over High-Level Game States in RTS Games


  • Alberto Uriarte Drexel University
  • Santiago Ontañón Drexel University



RTS, real-time strategy games, game-tree search, StarCraft, bot, AI, BWAPI


From an AI point of view, Real-Time Strategy (RTS) games are hard because they have enormous state spaces, they are real-time and partially observable. In this paper, we present an approach to deploy game-tree search in RTS games by using game state abstraction. We propose a high-level abstract representation of the game state, that significantly reduces the branching factor when used for game-tree search algorithms. Using this high-level representation, we evaluate versions of alpha-beta search and of Monte Carlo Tree Search (MCTS). We present experiments in the context of StarCraft showing promising results in dealing with the large branching factors present in RTS games.




How to Cite

Uriarte, A., & Ontañón, S. (2021). Game-Tree Search over High-Level Game States in RTS Games. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 10(1), 73-79.