%0 Conference Proceedings
%T Classification of Player Roles in the Team-Based Multi-player Game Dota 2
%+ University of Bremen
%A Eggert, Christoph
%A Herrlich, Marc
%A Smeddinck, Jan
%A Malaka, Rainer
%Z Part 1: Full papers
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 14th International Conference on Entertainment Computing (ICEC)
%C Trondheim, Norway
%Y Konstantinos Chorianopoulos
%Y Monica Divitini
%Y Jannicke Baalsrud Hauge
%Y Letizia Jaccheri
%Y Rainer Malaka
%I Springer International Publishing
%3 Entertainment Computing - ICEC 2015
%V LNCS-9353
%P 112-125
%8 2015-09-29
%D 2015
%R 10.1007/978-3-319-24589-8_9
%K multi-player games
%K player roles
%K classification
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 14
%2 https://inria.hal.science/hal-01758447/document
%2 https://inria.hal.science/hal-01758447/file/371182_1_En_9_Chapter.pdf
%L hal-01758447
%U https://inria.hal.science/hal-01758447
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-ICEC
%~ IFIP-TC14
%~ IFIP-LNCS-9353