Abstract
In the last years, the Video Game industry has growth considerably, capturing the attention of the research community. One of the research hot topics in videogames is related to the identification of gamers behaviour while they are playing the game. This work presents an initial case related to the identification of users behaviour in a particular kind of videogame through gamer interaction extraction and analysis. The Video Game selected in this work is a Tower Defence Game, called OTD, where the user needs to build towers, in a 2-D grid, to avoid the enemies to reach the exit point of the level. It has been created a framework that allows extract the information from the game and later use statistical techniques to analyse the gamers behaviour. Finally, some experiments have been carried out to test this framework.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Berns, A., Gonzalez-Pardo, A., Camacho, D.: Game-like language learning in 3-d virtual environments. Computers and Education 60(1), 210–220 (2013)
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)
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)
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)
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)
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)
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)
Gonzalez-Pardo, A., Palero, F., Camacho, D.: Micro and macro lemmings simulations based on ants colonies. In: Evostar. EvoGames (page in press, 2014)
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)
Johansson, A., Dell’Acqua, P.: Emotional behavior trees. In: 2012 IEEE Conference on Computational Intelligence and Games (CIG), pp. 355–362. IEEE (2012)
Karakovskiy, S., Togelius, J.: The mario ai benchmark and competitions. IEEE Transactions on Computational Intelligence and AI in Games 4(1), 55–67 (2012)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Palero, F., Gonzalez-Pardo, A., Camacho, D. (2015). Simple Gamer Interaction Analysis through Tower Defence Games. In: Camacho, D., Kim, SW., Trawiński, B. (eds) New Trends in Computational Collective Intelligence. Studies in Computational Intelligence, vol 572. Springer, Cham. https://doi.org/10.1007/978-3-319-10774-5_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-10774-5_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10773-8
Online ISBN: 978-3-319-10774-5
eBook Packages: EngineeringEngineering (R0)