| Abstract: |  Real-time strategy games are complex domains that require both fast reactive behavior and reasoninng about strategies. This work describes development of an artificial intelligence player for strategic combat scenario running in Open Real Time Strategy environment that uses two state-of-the-art methods, potential fields for unit movement and Monte-Carlo tree search for reasoning about strategies. Potential fields method is extended with path finding to avoid problems with local minima, Monte-Carlo tree search uses UCT selection strategy and progressive unprunning to limit number of expanded nodes. Resulting player was experimentally compared to other two existing players that are able to solve the same game scenario.
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