Modeling Player Experience for Content Creation

• Published in: IEEE transactions on computational intelligence and AI in games. Vol. 2; no. 1; pp. 54 - 67
• Main Authors: Pedersen, C., Togelius, J., Yannakakis, G.N.
• Format: Journal Article
• Language: English
• Published: IEEE 01.03.2010
• Subjects: Algorithm design and analysis; Artificial intelligence; Augmented reality; Computation; Computational intelligence; Content creation; Design optimization; Design parameters; fun; Game theory; Games; Intelligence; Learning; Mathematical model; Mathematical models; Multilayer perceptrons; neuroevolution; platform games; player experience; player satisfaction modeling; Players; Predictive models; preference learning; Technological innovation
• ISSN: 1943-068X; 1943-0698
• DOI: 10.1109/TCIAIG.2010.2043950

In this paper, we use computational intelligence techniques to built quantitative models of player experience for a platform game. The models accurately predict certain key affective states of the player based on both gameplay metrics that relate to the actions performed by the player in the game, and on parameters of the level that was played. For the experiments presented here, a version of the classic Super Mario Bros game is enhanced with parameterizable level generation and gameplay metrics collection. Player pairwise preference data is collected using forced choice questionnaires, and the models are trained using this data and neuroevolutionary preference learning of multilayer perceptrons (MLPs). The derived models will be used to optimize design parameters for particular types of player experience, allowing the designer to automatically generate unique levels that induce the desired experience for the player.