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Understanding Individual Play Sequences Using Growing Self Organizing Maps

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7663)

Abstract

When interacting with computer games, a player would derive a strategy to conquer the game using his/her experience, observations etc. During game play, the players adapt their strategy to better suit the challenges posed by the game. With time, these strategies would formulate to a pattern of interaction for an individual player with respect to myriad of game entities such as on handling of artificial opponents, movement strategies and decision making. Understanding these patterns would provide valuable insight about a player’s approach toward defeating the game which could be exploited to enhance the level of challenge posed by game AI. This paper attempts to identify dominant game play sequences made by an individual player by interpreting the positioning of the clusters in a growing self-organizing map (GSOM) generated using play data collected from the same player. Results indicate that dominant play sequences could indeed be identified but requires further analysis before a solid claim in this regard could be made.

Keywords

  • GSOM
  • Neural Networks
  • Pattern Recognition
  • Player Profiling

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References

  1. Charles, D., Kerr, A., McNeill, M., McAlister, M., Black, M., Kucklich, J., Moore, A., Stringer, K.: Player-Centred Game Design: Player Modelling and Adaptive Digital Games. In: Proceedings of the Digital Games Research Conference, vol. 285, Citeseer (2005)

    Google Scholar 

  2. Spronck, P.: A Model for Reliable Adaptive Game Intelligence. In: Proceedings of the International Joint Conference on Artificial Intelligence Workshop on Reasoning, Representation, and Learning in Computer Games, pp. 95–100 (2005)

    Google Scholar 

  3. Egnor, D.: Iocaine Powder. Int. Comput. Games Association J. 23, 33–35 (2000)

    Google Scholar 

  4. Billings, D., Davidson, A., Schaeffer, J., Szafron, D.: The Challenge of Poker. Artif. Intell. 134, 201–240 (2002)

    CrossRef  MATH  Google Scholar 

  5. Tychsen, A., Canossa, A.: Defining Personas in Games Using Metrics. In: Proceedings of the 2008 Conference on Future Play: Research, Play, Share, pp. 73–80. ACM (2008)

    Google Scholar 

  6. Drachen, A., Canossa, A., Yannakakis, G.N.: Player Modeling Using Self-Organization in Tomb Raider: Underworld. In: IEEE Symposium on Computational Intelligence and Games (CIG), pp. 1–8 (2009)

    Google Scholar 

  7. Lopes, R., Bidarra, R.: Adaptivity Challenges in Games and Simulations: A Survey IEEE Trans. Comput. Intell. AI Games 3, 85–99 (2011)

    CrossRef  Google Scholar 

  8. Wickramasinghe, M., Rajapakse, J., Alahakoon, D.: Investigating Individual Decision Making Patterns in Games Using Growing Self Organizing Maps. In: Anthony, P., Ishizuka, M., Lukose, D. (eds.) PRICAI 2012. LNCS (LNAI), vol. 7458, pp. 826–831. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  9. Wickramasinghe, M., Gunawardana, K., Rajapakse, J., Alahakoon, D.: Investigating Individual Game-Play Patterns Using a Self Organizing Map. In: Proceedings of the 6th International Conference on Information and Automation for Sustainability (ICIAfS). IEEE (forthcomming, 2012)

    Google Scholar 

  10. Alahakoon, D., Halgamuge, S.K., Srinivasan, B.: Dynamic Self-Organizing Maps with Controlled Growth for Knowledge Discovery. IEEE Trans. Neural Networks 11, 601–614 (2000)

    CrossRef  Google Scholar 

  11. Kohonen, T.: The self-organizing map. Proc. IEEE 78, 1464–1480 (1990)

    CrossRef  Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Wickramasinghe, M., Rajapakse, J., Alahakoon, D. (2012). Understanding Individual Play Sequences Using Growing Self Organizing Maps. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34475-6_7

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  • DOI: https://doi.org/10.1007/978-3-642-34475-6_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34474-9

  • Online ISBN: 978-3-642-34475-6

  • eBook Packages: Computer ScienceComputer Science (R0)