Multi-scale Bayesian modeling for RTS games: an application to StarCraft AI

Abstract : This paper showcases the use of Bayesian models for real-time strategy (RTS) games AI in three distinct core components: micro-management (units control), tactics (army moves and positions), and strategy (economy, technology, production, army types). The strength of having end-to-end probabilistic models is that distributions on specific variables can be used to inter-connect different models at different levels of abstraction. We applied this modeling to StarCraft, and evaluated each model independently. Along the way, we produced and released a comprehensive dataset for RTS machine learning.
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IEEE Transactions on Computational Intelligence and AI in games, IEEE Computational Intelligence Society, 2015, 〈10.1109/TCIAIG.2015.2487743〉
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Gabriel Synnaeve, Pierre Bessiere. Multi-scale Bayesian modeling for RTS games: an application to StarCraft AI. IEEE Transactions on Computational Intelligence and AI in games, IEEE Computational Intelligence Society, 2015, 〈10.1109/TCIAIG.2015.2487743〉. 〈hal-01228961〉

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