Online Gamers Classification Using K-means

  • Fernando Palero
  • Cristian Ramirez-Atencia
  • David Camacho
Conference paper

DOI: 10.1007/978-3-319-10422-5_22

Part of the Studies in Computational Intelligence book series (SCI, volume 570)
Cite this paper as:
Palero F., Ramirez-Atencia C., Camacho D. (2015) Online Gamers Classification Using K-means. In: Camacho D., Braubach L., Venticinque S., Badica C. (eds) Intelligent Distributed Computing VIII. Studies in Computational Intelligence, vol 570. Springer, Cham

Abstract

In order to achieve flow and increase player retention, it is important that games difficulty matches player skills. Being able to evaluate how people play a game is a crucial component for detecting gamers strategies in video-games. One of themain 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, K-Means). Finally, we will study the similitude between several gameplays where players use different strategies.

Keywords

Player Strategies Video Games Sliding Windows K-Means Real Time Strategy Game 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Fernando Palero
    • 1
  • Cristian Ramirez-Atencia
    • 1
  • David Camacho
    • 1
  1. 1.Computer Science DepartmentUniversidad Autónoma de MadridMadridSpain

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