Simple Gamer Interaction Analysis through Tower Defence Games (original) (raw)

References

  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. Berns, A., Gonzalez-Pardo, A., Camacho, D.: Game-like language learning in 3-d virtual environments. Computers and Education 60(1), 210–220 (2013)
    Article Google Scholar
  3. 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
  4. Drachen, A., Canossa, A., Yannakakis, G.N.: Player modeling using selforganization in tomb raider: Underworld. In: Proceedings of the 5th International Conference on Computational Intelligence and Games, CIG 2009, pp. 1–8. IEEE Press, Piscataway (2009)
    Google Scholar
  5. Drachen, A., Rafet, S., Bauckhage, C., Thurau, C.: Guns, swords and data: Clustering of player behavior in computer games in the wild. In: Proceedings of CIG 2012, pp. 163–170. IEEE (2012)
    Google Scholar
  6. Drachen, A., Canossa, A.: Towards gameplay analysis via gameplay metrics. In: Proceedings of the 13th International MindTrek Conference: Everyday Life in the Ubiquitous Era, pp. 202–209. ACM (2009)
    Google Scholar
  7. 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
  8. 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
  9. Gonzalez-Pardo, A., Palero, F., Camacho, D.: Micro and macro lemmings simulations based on ants colonies. In: Evostar. EvoGames (page in press, 2014)
    Google Scholar
  10. Gonzalez-Pardo, A., Rosa, A., Camacho, D.: Behaviour-based identification of student communities in virtual worlds. Computer Science and Information Systems 11(1), 195–213 (2014)
    Article Google Scholar
  11. Johansson, A., Dell’Acqua, P.: Emotional behavior trees. In: 2012 IEEE Conference on Computational Intelligence and Games (CIG), pp. 355–362. IEEE (2012)
    Google Scholar
  12. Karakovskiy, S., Togelius, J.: The mario ai benchmark and competitions. IEEE Transactions on Computational Intelligence and AI in Games 4(1), 55–67 (2012)
    Article Google Scholar
  13. Nguyen, K.Q., Wang, Z., Thawonmas, R.: Potential flows for controlling scout units in starcraft. In: 2013 IEEE Conference on Computational Intelligence in Games (CIG), pp. 1–7. IEEE (2013)
    Google Scholar
  14. 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
  15. Powley, E.J., Whitehouse, D., Cowling, P.I.: Monte carlo tree search with macro-actions and heuristic route planning for the physical travelling salesman problem. In: 2012 IEEE Conference on Computational Intelligence and Games (CIG), pp. 234–241. IEEE (2012)
    Google Scholar
  16. Rosenthal, C., Congdon, C.B.: Personality profiles for generating believable bot behaviors. In: 2012 IEEE Conference on Computational Intelligence and Games (CIG), pp. 124–131. IEEE (2012)
    Google Scholar
  17. Schaul, T.: A video game description language for model-based or interactive learning. In: 2013 IEEE Conference on Computational Intelligence in Games (CIG), pp. 1–8. IEEE (2013)
    Google Scholar
  18. Shaker, N., Togelius, J., Yannakakis, G.N., Weber, B., Shimizu, T., Hashiyama, T., Sorenson, N., Pasquier, P., Mawhorter, P., Takahashi, G., et al.: The 2010 mario ai championship: Level generation track. IEEE Transactions on Computational Intelligence and AI in Games 3(4), 332–347 (2011)
    Article Google Scholar
  19. Sifa, R., Bauckhage, C.: Archetypical motion: Supervised game behavior learning with archetypal analysis. In: 2013 IEEE Conference on Computational Intelligence in Games (CIG), pp. 1–8. IEEE (2013)
    Google Scholar
  20. 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
  21. Thompson, J.J., Blair, M.R., Chen, L., Henrey, A.: Video game telemetry as a critical tool in the study of complex skill learning. PLoS One 8(18), 1–12 (2013)
    Google Scholar
  22. 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
  23. Yannakakis, G.N., Maragoudakis, M.: Player modeling impact on player’s entertainment in computer games. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, pp. 74–78. Springer, Heidelberg (2005)
    Chapter Google Scholar
  24. Young, J., Smith, F., Atkinson, C., Poyner, K., Chothia, T.: Scail: An integrated starcraft ai system. In: 2012 IEEE Conference on Computational Intelligence and Games (CIG), pp. 438–445. IEEE (2012)
    Google Scholar

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