Global State Evaluation in StarCraft
DOI:
https://doi.org/10.1609/aiide.v10i1.12725Keywords:
Machine Learning, Opponent Modelling, StarCraft, Real-Time StrategyAbstract
State evaluation and opponent modelling are important areasto consider when designing game-playing Artificial Intelligence.This paper presents a model for predicting whichplayer 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 skillwhich can be used a features in the predictive model, includingusing a battle simulation as a baseline to compare playerperformance against.
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Published
2021-06-29
How to Cite
Erickson, G., & Buro, M. (2021). Global State Evaluation in StarCraft. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 10(1), 112-118. https://doi.org/10.1609/aiide.v10i1.12725
Issue
Section
Poster Papers