The research goals of CEPEG are to model in real time computer game players/users satisfaction and strategies for commercial games using information available from the game platform, and to construct software able to do this. To achieve these goals the plan is to devise a method that efficiently identifies qualitative features contributing to player satisfaction;design quantitative measures for these features;investigate the correlation between the type of player, the real-time satisfaction estimation and the quantitative feature values and develop and implement AI techniques based on user modelling and machine learning that will augment the entertainment value of the game in real-time.
G. N. Yannakakis, and J. Hallam, “Real-time Game Adaptation for Optimizing Player Satisfaction,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 1, issue 2, pp. 121-133, June 2009. Pdf.
G. N. Yannakakis, M. Maragoudakis, and J. Hallam, “Preference Learning for Cognitive Modeling: A Case Study on Entertainment Preferences,” IEEE Systems, Man and Cybernetics; Part A: Systems and Humans, vol. 39, no. 6, pp. 1165-1175, November 2009. pdf
G. N. Yannakakis, and J. Hallam, “Entertainment Modeling through Physiology in Physical Play,” International Journal of Human-Computer Studies, vol. 66, issue 10, pp. 741-755, October 2008. pdf
G. N. Yannakakis, J. Hallam and H. H. Lund, “Entertainment Capture through Heart Rate Activity in Physical Interactive Playgrounds,” User Modeling and User-Adapted Interaction, Special Issue on Affective Modeling and Adaptation, vol. 18, no. 1-2, pp. 207-243, February 2008.<a href="http://www.itu.dk/~yannakakis/UMUAI08.pdf"pdf
G. N. Yannakakis, and J. Hallam, “Modeling and Augmenting Game Entertainment through Challenge and Curiosity,” International Journal on Artificial Intelligence Tools, vol. 16, issue 6, pp. 981-999, December 2007.
G. N. Yannakakis, and J. Hallam, “Towards Optimizing Entertainment in Computer Games,” Applied Artificial Intelligence, 21:933-971, 2007.