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Improving StarCraft II Player League Prediction with Macro-Level Features

Chen, Yinheng; Aitchison, Matthew; Sweetser Kyburz, Penny


Accurate player skill modelling is an important but challenging task in Real-Time Strategy Games. Previous efforts have relied strongly on micromanagement features, such as Actions Per Minute, producing limited results. In this paper, we present an improved player skill classifier for StarCraft II that predicts, from a replay, a player's exact league at 61.7% accuracy, or within one league at 94.5%, outperforming the previous state of the art of 47.3%. Unlike previous classifiers, our...[Show more]

CollectionsANU Research Publications
Date published: 2020-11
Type: Conference paper
Book Title: AI 2020: Advances in Artificial Intelligence
33rd Australasian Joint Conference, AI 2020, Canberra, ACT, Australia, November 29–30, 2020, Proceedings
DOI: 10.1007/978-3-030-64984-5
Access Rights: Open Access after embargo


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