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Special Tactics: a Bayesian Approach to Tactical Decision-making

Abstract : We describe a generative Bayesian model of tactical attacks in strategy games, which can be used both to predict attacks and to take tactical decisions. This model is designed to easily integrate and merge information from other (probabilistic) estimations and heuristics. In particular, it handles uncertainty in enemy units' positions as well as their probable tech tree. We claim that learning, being it supervised or through reinforcement, adapts to skewed data sources. We evaluated our approach on StarCraft 1 : the parameters are learned on a new (freely available) dataset of game states, deterministically re-created from replays, and the whole model is evaluated for prediction in realistic conditions. It is also the tactical decision-making component of our StarCraft AI competition bot.
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Contributor : Gabriel Synnaeve Connect in order to contact the contributor
Submitted on : Friday, November 16, 2012 - 3:20:15 PM
Last modification on : Wednesday, July 6, 2022 - 4:14:46 AM
Long-term archiving on: : Saturday, December 17, 2016 - 10:52:15 AM


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  • HAL Id : hal-00752841, version 1



Gabriel Synnaeve, Pierre Bessière. Special Tactics: a Bayesian Approach to Tactical Decision-making. Proceedings of the IEEE Conference on Computational Intelligence and Games, Sep 2012, Granada, Spain. pp.978-1-4673-1194-6/12/ 409-416. ⟨hal-00752841⟩



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