A Bayesian model for opening prediction in RTS games with application to StarCraft

Gabriel Synnaeve, Pierre Bessiere
2011 2011 IEEE Conference on Computational Intelligence and Games (CIG'11)  
This paper presents a Bayesian model to predict the opening (first strategy) of opponents in real-time strategy (RTS) games. Our model is general enough to be applied to any RTS game with the canonical gameplay of gathering resources to extend a technology tree and produce military units and we applied it to StarCraft 1 . This model can also predict the possible technology trees of the opponent, but we will focus on openings here. The parameters of this model are learned from replays (game
more » ... , labeled with openings. We present a semisupervised method of labeling replays with the expectationmaximization algorithm and key features, then we use these labels to learn our parameters and benchmark our method with cross-validation. Uses of such a model range from a commentary assistant (for competitive games) to a core component of a dynamic RTS bot/AI, as it will be part of our StarCraft AI competition entry bot.
doi:10.1109/cig.2011.6032018 dblp:conf/cig/SynnaeveB11a fatcat:ca6qp3aryfbmhiddo4jr7gmhmu