Imperial College London

Professor Yiannis Demiris

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Professor of Human-Centred Robotics, Head of ISN
 
 
 
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Contact

 

+44 (0)20 7594 6300y.demiris Website

 
 
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Location

 

1014Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Georgiou:2017:10.1007/s11257-017-9192-3,
author = {Georgiou, T and Demiris, Y and Georgiou, T and Demiris, Y and Georgiou, T and Demiris, Y and Georgiou, T and Demiris, Y},
doi = {10.1007/s11257-017-9192-3},
journal = {USER MODELING AND USER-ADAPTED INTERACTION},
pages = {267--311},
title = {Adaptive user modelling in car racing games using behavioural and physiological data},
url = {http://dx.doi.org/10.1007/s11257-017-9192-3},
volume = {27},
year = {2017}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - © 2017, The Author(s). Personalised content adaptation has great potential to increase user engagement in video games. Procedural generation of user-tailored content increases the self-motivation of players as they immerse themselves in the virtual world. An adaptive 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 identify 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 primitives. 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 feedback 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 personalised 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.
AU - Georgiou,T
AU - Demiris,Y
AU - Georgiou,T
AU - Demiris,Y
AU - Georgiou,T
AU - Demiris,Y
AU - Georgiou,T
AU - Demiris,Y
DO - 10.1007/s11257-017-9192-3
EP - 311
PY - 2017///
SN - 0924-1868
SP - 267
TI - Adaptive user modelling in car racing games using behavioural and physiological data
T2 - USER MODELING AND USER-ADAPTED INTERACTION
UR - http://dx.doi.org/10.1007/s11257-017-9192-3
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000401074900003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - http://hdl.handle.net/10044/1/48453
VL - 27
ER -