Theodosis Georgiou, Yiannis Demiris
Personalised content adaptation has great potential to increase user engage- ment in video games. Procedural generation of user-tailored content increases the self-motivation of players as they immerse themselves in the virtual world. An adap- tive user model is needed to capture the skills of the player and enable automatic game content altering algorithms to fit the individual user. We propose an adaptive user modelling approach using a combination of unobtrusive physiological data to iden- tify strengths and weaknesses in user performance in car racing games. Our system creates user-tailored tracks to improve driving habits and user experience, and to keep engagement at high levels. The user modelling approach adopts concepts from the Trace Theory framework; it uses machine learning to extract features from the user’s physiological data and game-related actions, and cluster them into low level primi- tives. These primitives are transformed and evaluated into higher level abstractions such as experience, exploration and attention. These abstractions are subsequently used to provide track alteration decisions for the player. Collection of data and feed- back from 52 users allowed us to associate key model variables and outcomes to user responses, and to verify that the model provides statistically significant decisions per- sonalised to the individual player. Tailored game content variations between users in our experiments, as well as the correlations with user satisfaction demonstrate that our algorithm is able to automatically incorporate user feedback in subsequent procedural content generation. B Theodosis Georgiou theodosis.georgiou08@imperial.ac.uk Yiannis Demiris y.demiris@imperial.ac.uk 1 Personal Robotics Laboratory, Department of Electrical and Electronic Engineering, Imperial College London, Exhibition Road, South Kensington, London SW7 2BT, UK 123 268 T. Georgiou, Y. Demiris