Global State Evaluation in StarCraft
نویسندگان
چکیده
State evaluation and opponent modelling are important areas to consider when designing game-playing Artificial Intelligence. This paper presents a model for predicting which player will win in the real-time strategy game StarCraft. Model weights are learned from replays using logistic regression. We also present some metrics for estimating player skill which can be used a features in the predictive model, including using a battle simulation as a baseline to compare player performance against.
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