In: Proceedings of the International Conference on the Foundations of Digital Games (2010) Smith, G., Whitehead, J., Mateas, M.: Tanagra: A mixed-initiative level design tool. In: Sandbox 2008: Proceedings of the 2008 ACM SIGGRAPH Symposium on Video Games, pp. ![]() Smith, G., Cha, M., Whitehead, J.: A framework for analysis of 2d platformer levels. IEEE Transactions on Computational Intelligence and Games (2011) Shaker, N., Togelius, J., Yannakakis, G.N., Weber, B., Shimizu, T., Hashiyama, T., Sorenson, N., Pasquier, P., Mawhorter, P., Takahashi, G., Smith, G., Baumgarten, R.: The 2010 Mario AI championship: Level generation track. IEEE Transactions on Computational Intelligence and AI in Games, CIG (2011) Shaker, N., Yannakakis, G.N., Togelius, J.: Feature Analysis for Modeling Game Content Quality. In: Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE). Shaker, N., Yannakakis, G.N., Togelius, J.: Towards Automatic Personalized Content Generation for Platform Games. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcázar, A.I., Merelo, J.J., Neri, F., Preuss, M., Richter, H., Togelius, J., Yannakakis, G.N. Perez, D., Nicolau, M., O’Neill, M., Brabazon, A.: Evolving Behaviour Trees for the Mario AI Competition Using Grammatical Evolution. IEEE Transactions on Computational Intelligence and AI in Games 2(1), 54–67 (2010) Pedersen, C., Togelius, J., Yannakakis, G.N.: Modeling player experience for content creation. In: CIG 2009: Proceedings of the 5th International Conference on Computational Intelligence and Games, pp. Pedersen, C., Togelius, J., Yannakakis, G.N.: Modeling player experience in super mario bros. In: Proceedings of the Joint Conference on Easier and More Productive use of Computer Systems (Part - II): Human Interface and the user Interface, CHI 1981, vol. 1981, p. Malone, T.: What makes computer games fun (abstract only). Koster, R.: A theory of fun for game design. International Journal of Computer Games Technology 1 (2010) Kazmi, S., Palmer, I.: Action recognition for support of adaptive gameplay: A case study of a first person shooter. In: Proceedings of the 2010 Workshop on Procedural Content Generation in Games, PCGames 2010, pp. Jennings-Teats, M., Smith, G., Wardrip-Fruin, N.: Polymorph: dynamic difficulty adjustment through level generation. In: FDG 2010: Proceedings of the Fifth International Conference on the Foundations of Digital Games, pp. Hullett, K., Whitehead, J.: Design patterns in fps levels. ![]() Springer, Heidelberg (2011)Ĭardamone, L., Loiacono, D., Lanzi, P.L.: Interactive evolution for the procedural generation of tracks in a high-end racing game. 83–90 (2010)Ĭardamone, L., Yannakakis, G.N., Togelius, J., Lanzi, P.L.: Evolving Interesting Maps for a First Person Shooter. In: 2010 IEEE Symposium on Computational Intelligence and Games (CIG), pp. Cengage Learning (2005)īojarski, S., Congdon, C.: Realm: A rule-based evolutionary computation agent that learns to play mario. This process is experimental and the keywords may be updated as the learning algorithm improves.ījörk, S., Holopainen, J.: Patterns in game design. These keywords were added by machine and not by the authors. Several types of features are explored, including item frequencies and patterns extracted through frequent sequence mining. ![]() We analyse data from players playing 780 pairs of short game sessions of the platform game Super Mario Bros, investigate the impact of the session size and what part of the level that has the major affect on player experience. This papers reports on further refinement of a method to understand this relationship by analysing data collected from players, building models that predict player experience and analysing what features of game and player data predict player affect best. Analysing the relationship between game content, player behaviour and self-reported affective states constitutes an important step towards understanding game experience and constructing effective game adaptation mechanisms. A recent trend within computational intelligence and games research is to investigate how to affect video game players’ in-game experience by designing and/or modifying aspects of game content.
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