BibTex format

author = {Georgiou, T and Demiris, Y},
doi = {10.1109/CIG.2016.7860435},
publisher = {IEEE},
title = {Personalised Track Design in Car Racing Games},
url = {},
year = {2017}

RIS format (EndNote, RefMan)

AB - Real-time adaptation of computer games’ content tothe users’ skills and abilities can enhance the player’s engagementand immersion. Understanding of the user’s potential whileplaying is of high importance in order to allow the successfulprocedural generation of user-tailored content. We investigatehow player models can be created in car racing games. Our usermodel uses a combination of data from unobtrusive sensors, whilethe user is playing a car racing simulator. It extracts featuresthrough machine learning techniques, which are then used tocomprehend the user’s gameplay, by utilising the educationaltheoretical frameworks of the Concept of Flow and Zone ofProximal Development. The end result is to provide at a nextstage a new track that fits to the user needs, which aids boththe training of the driver and their engagement in the game.In order to validate that the system is designing personalisedtracks, we associated the average performance from 41 usersthat played the game, with the difficulty factor of the generatedtrack. In addition, the variation in paths of the implementedtracks between users provides a good indicator for the suitabilityof the system.
AU - Georgiou,T
AU - Demiris,Y
DO - 10.1109/CIG.2016.7860435
PY - 2017///
SN - 2325-4289
TI - Personalised Track Design in Car Racing Games
UR -
UR -
ER -