%0 Conference Proceedings
%T Identifying Rush Strategies Employed in StarCraft II Using Support Vector Machines
%+ Université de Tsukuba = University of Tsukuba
%+ The University of Tokyo (UTokyo)
%A Budianto, Teguh
%A Oh, Hyunwoo
%A Ding, Yi
%A Long, Zi
%A Utsuro, Takehito
%Z Part 8: Poster and Interactive Session
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 16th International Conference on Entertainment Computing (ICEC)
%C Tsukuba City, Japan
%Y Nagisa Munekata
%Y Itsuki Kunita
%Y Junichi Hoshino
%I Springer International Publishing
%3 Entertainment Computing – ICEC 2017
%V LNCS-10507
%P 357-361
%8 2017-09-18
%D 2017
%R 10.1007/978-3-319-66715-7_39
%K Real-time strategy game
%K StarCraft II
%K Rush strategy
%Z Computer Science [cs]Conference papers
%X This paper studies the strategies used in StarCraft II, a real-time strategy game (RTS) wherein two sides fight against each other in a battlefield context. We propose an approach which automatically classifies StarCraft II game-log collections into rush and non-rush strategies using a support vector machine (SVM). To achieve this, three types of features are evaluated: (i) the upper bound of variance in time series for the numbers of workers, (ii) the upper bound of the numbers of workers at a specific time, and (iii) the lower bound of the start time for building the second base. Thus, by evaluating these features, we obtain the optimal parameters combinations.
%G English
%Z TC 14
%2 https://inria.hal.science/hal-01771286/document
%2 https://inria.hal.science/hal-01771286/file/978-3-319-66715-7_39_Chapter.pdf
%L hal-01771286
%U https://inria.hal.science/hal-01771286
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-ICEC
%~ IFIP-TC14
%~ IFIP-LNCS-10507