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Classification of Player Roles in the Team-Based Multi-player Game Dota 2

Abstract : Computer games are big business, which is also reflected in the growing interest in competitive gaming, the so-called electronic sports. Multi-player online battle arena games are among the most successful games in this regard. In order to execute complex team-based strategies, players take on very specific roles within a team. This paper investigates the applicability of supervised machine learning to classifying player behavior in terms of specific and commonly accepted but not formally well-defined roles within a team of players of the game Dota 2. We provide an in-depth discussion and novel approaches for constructing complex attributes from low-level data extracted from replay files. Using attribute evaluation techniques, we are able to reduce a larger set of candidate attributes down to a manageable number. Based on this resulting set of attributes, we compare and discuss the performance of a variety of supervised classification algorithms. Our results with a data set of 708 labeled players see logistic regression as the overall most stable and best performing classifier.
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Christoph Eggert, Marc Herrlich, Jan Smeddinck, Rainer Malaka. Classification of Player Roles in the Team-Based Multi-player Game Dota 2. 14th International Conference on Entertainment Computing (ICEC), Sep 2015, Trondheim, Norway. pp.112-125, ⟨10.1007/978-3-319-24589-8_9⟩. ⟨hal-01758447⟩

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