Online Gamers Classification using K-means
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Springer International PublishingDate
2015Citation
10.1007/978-3-319-10422-5_22
Intelligent Distributed Computing VIII. Studies in Computational Intelligence, Volumen 570. Springer, 2015. 201-208
ISSN
1860-949XISBN
978-3-319-10421-8 (print); 978-3-319-10422-5 (online)DOI
10.1007/978-3-319-10422-5_22Editor's Version
http://dx.doi.org/10.1007/978-3-319-10422-5 22Subjects
Player Strategies; Video Games; Sliding Windows; K-Means; Real Time Strategy Game; InformáticaNote
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-10422-5_22Rights
© Springer-Verlag Berlin Heidelberg 2015Abstract
In order to achieve flow and increase player retention, it is important
that games di culty matches player skills. Being able to evaluate how people play a game is a crucial component for detecting gamers strategies in videogames.
One of the main problems in player strategy detection is whether attributes selected to define strategies correctly detect the actions of the player. In this paper, we will study a Real Time Strategy (RTS) game. In RTS the participants make use of units and structures to secure areas of a map and/or destroy the opponents resources. We will extract real-time information about the players strategies at
several gameplays through a Web Platform. After gathering enough information, the model will be evaluated in terms of unsupervised learning (concretely, KMeans).
Finally, we will study the similitude between several gameplays where
players use di erent strategies.
Files in this item
Google Scholar:Fernando, Palero
-
Ramirez-Atencia, Cristian
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Camacho, David
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