Personalised track design in car racing games

Abstract

Real-time adaptation of computer games' content to the users' skills and abilities can enhance the player's engagement and immersion. Understanding of the user's potential while playing is of high importance in order to allow the successful procedural generation of user-tailored content. We investigate how player models can be created in car racing games. Our user model uses a combination of data from unobtrusive sensors, while the user is playing a car racing simulator. It extracts features through machine learning techniques, which are then used to comprehend the user's gameplay, by utilising the educational theoretical frameworks of the Concept of Flow and Zone of Proximal Development. The end result is to provide at a next stage a new track that fits to the user needs, which aids both the training of the driver and their engagement in the game. In order to validate that the system is designing personalised tracks, we associated the average performance from 41 users that played the game, with the difficulty factor of the generated track. In addition, the variation in paths of the implemented tracks between users provides a good indicator for the suitability of the system.