research-article

TrackGen

Authors:

Luigi Cardamone,

Pier Luca Lanzi,

Daniele LoiaconoAuthors Info & Claims

Applied Soft Computing, Volume 28, Issue C

Pages 550 - 558

https://doi.org/10.1016/j.asoc.2014.11.010

Published: 01 March 2015 Publication History

Abstract

Graphical abstractDisplay Omitted HighlightsTrackGen is an online tool for the generation of tracks for two open-source 3D car racing games.It integrates interactive evolution with procedural content generation.The system is online since January 2012 at http://trackgen.pierlucalanzi.net.Since January 2012, TrackGen had more than 7600 unique visitors, corresponding to 85,500 page views, and it was employed to evolve more than 8853 tracks. TrackGen is an online tool for the generation of tracks for two open-source 3D car racing games (TORCS and Speed Dreams). It integrates interactive evolution with procedural content generation and comprises two components: (i) a web frontend that maintains the database of all the evolved populations and manages the interaction with users (by collecting users evaluations and providing access to the evolved tracks) and (ii) an evolutionary/content-generation backend that runs both the evolutionary algorithm and generates the actual game content that is available through the web frontend. The first prototype of the tool was presented in July 2011 but advertised only to researchers; the first official version which generated tracks only for TORCS was released to the game community in September 2011; due to the many requests, we released a new version soon afterwards, in January 2012, with support for Speed Dreams (the fork of TORCS focused on visual realism and graphic quality) that has been online since then. From January 2012 until July 2014, TrackGen had more than 7600 unique visitors who visited the website around 11,500 times and viewed 85,500 pages; it was employed to evolve more than 8853 tracks, and it was used to download 12,218 tracks. Some of the tracks evolve by our system have been also included in the TORCS distribution.

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Cited By

Majid MCrawfis R(2024)A Framework to Even-Out Racetrack BiasProceedings of the 19th International Conference on the Foundations of Digital Games10.1145/3649921.3659849(1-8)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3649921.3659849
Feng BSu MZeng KLi X(2024)Player-Oriented Procedural Generation: Producing Desired Game Content by Natural LanguageHCI in Games10.1007/978-3-031-60692-2_18(260-274)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1007/978-3-031-60692-2_18
Lanzi PLoiacono DSilva SPaquete L(2023)ChatGPT and Other Large Language Models as Evolutionary Engines for Online Interactive Collaborative Game DesignProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590351(1383-1390)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590351
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Published In

cover image Applied Soft Computing

Applied Soft Computing Volume 28, Issue C

March 2015

608 pages

ISSN:1568-4946

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Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 March 2015

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Cited By

Majid MCrawfis R(2024)A Framework to Even-Out Racetrack BiasProceedings of the 19th International Conference on the Foundations of Digital Games10.1145/3649921.3659849(1-8)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3649921.3659849
Feng BSu MZeng KLi X(2024)Player-Oriented Procedural Generation: Producing Desired Game Content by Natural LanguageHCI in Games10.1007/978-3-031-60692-2_18(260-274)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1007/978-3-031-60692-2_18
Lanzi PLoiacono DSilva SPaquete L(2023)ChatGPT and Other Large Language Models as Evolutionary Engines for Online Interactive Collaborative Game DesignProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590351(1383-1390)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590351
Kruse JConnor AMarks S(2022)Evaluation of a Multi-agent “Human-in-the-loop” Game Design SystemACM Transactions on Interactive Intelligent Systems10.1145/353100912:3(1-26)Online publication date: 26-Jul-2022
https://dl.acm.org/doi/10.1145/3531009
Liu SChaoran LYue LHeng MXiao HYiming SLicong WZe CXianghao GHengtong LYu DQinting TDeterding SKhosmood FPirker JApperley T(2019)Automatic generation of tower defense levels using PCGProceedings of the 14th International Conference on the Foundations of Digital Games10.1145/3337722.3337723(1-9)Online publication date: 26-Aug-2019
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Gambi AMueller MFraser GZhang DMøller A(2019)Automatically testing self-driving cars with search-based procedural content generationProceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3293882.3330566(318-328)Online publication date: 10-Jul-2019
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Ruela AGuimarães F(2017)Procedural generation of non-player characters in massively multiplayer online strategy gamesSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2238-321:23(7005-7020)Online publication date: 1-Dec-2017
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