Online Gamers Classification Using K-means

  • Fernando Palero
  • Cristian Ramirez-Atencia
  • David Camacho
Part of the Studies in Computational Intelligence book series (SCI, volume 570)

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 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alayed, H., Frangoudes, F., Neuman, C.: Behavioral-based cheating detection in online first person shooters using machine learning techniques. In: 2013 IEEE Conference on Computational Intelligence in Games (CIG), pp. 1–8. IEEE (2013)Google Scholar
  2. 2.
    Alsabti, K., Ranka, S., Singh, V.: An efficient k-means clustering algorithm. Association for the Advancement of Artificial Intelligence (1997)Google Scholar
  3. 3.
    Bello-Orgaz, G., Menendez, H., Camacho, D.: Adaptive k-means algorithm for overlapped graph clustering. International Journal of Neural Systems 22(05), 1–19 (2012)CrossRefGoogle Scholar
  4. 4.
    Brzezinski, D.: Mining Data Streams with Concept Drift. Master’s thesis, Poznan University of Technology (2010)Google Scholar
  5. 5.
    Dey, R., Child, C.: QL-BT: Enhancing behaviour tree design and implementation with Q-learning. In: 2013 IEEE Conference on Computational Intelligence in Games (CIG), pp. 1–8. IEEE (2013)Google Scholar
  6. 6.
    Gagne, D.J., Congdon, C.B.: Fright: A flexible rule-based intelligent ghost team for Ms. Pac-Man. In: 2012 IEEE Conference on Computational Intelligence and Games (CIG), pp. 273–280. IEEE (2012)Google Scholar
  7. 7.
    Gonzalez-Pardo, A., Palero, F., Camacho, D.: An empirical study on collective intelligence algorithms for vide games problem-solving. Computing and Informatics (In press, 2014)Google Scholar
  8. 8.
    Palero, F., Gonzalez-Pardo, A., Camacho, D.: Simple Gamer Interaction Analysis through Tower Defence Games (submited, 2014)Google Scholar
  9. 9.
    Pedersen, C., Togelius, J., Yannakakis, G.N.: Modeling player experience in Super Mario Bros. In: IEEE Symposium on Computational Intelligence and Games, CIG 2009, pp. 132–139. IEEE (2009)Google Scholar
  10. 10.
    Polceanu, M.: MirrorBot: Using human-inspired mirroring behavior to pass a Turing test. In: 2013 IEEE Conference on Computational Intelligence in Games (CIG), pp. 1–8. IEEE (2013)Google Scholar
  11. 11.
    Ray, S., Turi, R.H.: Determination of number of clusters in k-means clustering and application in colour image segmentation. In: Proceedings of the 4th International Conference on Advances in Pattern Recognition and Digital Techniques, pp. 137–143 (1999)Google Scholar
  12. 12.
    Synnaeve, G., Bessiere, P.: A Bayesian model for RTS units control applied to StarCraft. In: 2011 IEEE Conference on Computational Intelligence and Games (CIG), pp. 190–196. IEEE (2011)Google Scholar
  13. 13.
    Traish, J.M., Tulip, J.R.: Towards adaptive online RTS AI with NEAT. In: 2012 IEEE Conference on Computational Intelligence and Games (CIG), pp. 430–437. IEEE (2012)Google Scholar
  14. 14.
    Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2003, pp. 226–235. ACM, New York (2003)Google Scholar
  15. 15.
    Yannakakis, G.N., Hallam, J.: Feature selection for capturing the experience of fun. In: Proceedings of the Artificial Intelligence and Interactive Digital Entertainment, vol. 7, pp. 37–42 (2007)Google Scholar

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

Personalised recommendations