Personalised Track Design in Car Racing Games
File(s)
Accepted version
Author(s)
Georgiou, T
Demiris, Y
Type
Conference Paper
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.
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.
Date Issued
2017-02-23
Date Acceptance
2016-06-14
Citation
IEEE Computational Intelligence and Games 2016, 2017
ISSN
2325-4289
Publisher
IEEE
Journal / Book Title
IEEE Computational Intelligence and Games 2016
Copyright Statement
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Source
Computational Intelligence and Games
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
Publication Status
Published
Start Date
2016-09-20
Finish Date
2016-09-23
Coverage Spatial
Santorini, Greece