Classification of Player Roles in the Team-Based Multi-player Game Dota 2 - Entertainment Computing – ICEC 2015 Access content directly
Conference Papers Year : 2015

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.
Fichier principal
Vignette du fichier
371182_1_En_9_Chapter.pdf (825.5 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01758447 , version 1 (04-04-2018)

Licence

Attribution

Identifiers

Cite

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⟩
193 View
2180 Download

Altmetric

Share

Gmail Facebook X LinkedIn More