TY - JOUR
T1 - General video game AI
T2 - A multitrack framework for evaluating agents, games, and content generation algorithms
AU - Perez-Liebana, Diego
AU - Liu, Jialin
AU - Khalifa, Ahmed
AU - Gaina, Raluca D.
AU - Togelius, Julian
AU - Lucas, Simon M.
N1 - Funding Information:
Manuscript received February 26, 2018; revised July 27, 2018 and December 22, 2018; accepted February 17, 2019. Date of publication March 11, 2019; date of current version September 13, 2019. This work was supported in part by the EPSRC CDT in Intelligent Games and Game Intelligence under Grant EP/L015846/1, in part by the Shenzhen Peacock Plan under Grant KQTD2016112514355531, in part by the Science and Technology Innovation Committee Foundation of Shenzhen under Grant ZDSYS201703031748284, and in part by the Program for University Key Laboratory of Guangdong Province under Grant 2017KSYS008. (Corresponding author: Jialin Liu.) D. Perez-Liebana, R. D. Gaina, and S. M. Lucas are with the Department of Electrical Engineering and Computer Engineering (EECS), Queen Mary University of London, London E1 4NS, U.K. (e-mail:,diego.perez@qmul.ac.uk; r.d.gaina@qmul.ac.uk; simon.lucas@qmul.ac.uk).
Publisher Copyright:
© 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications standards/publications/rights/index.html for more information.
PY - 2019/9
Y1 - 2019/9
N2 - General video game playing aims at designing an agent that is capable of playing multiple video games with no human intervention. In 2014, the General Video Game Artificial Intelligence (GVGAI) competition framework was created and released with the purpose of providing researchers a common open-source and easy-to-use platform for testing their artificial intelligence (AI) methods with potentially infinity of games created using the video game description language (VGDL). The framework has been expanded into several tracks during the last few years to meet the demands of different research directions. The agents are required either to play multiple unknown games with or without access to game simulations, or to design new game levels or rules. This survey paper presents the VGDL, the GVGAI framework, existing tracks, and reviews the wide use of GVGAI framework in research, education, and competitions five years after its birth. A future plan of framework improvements is also described.
AB - General video game playing aims at designing an agent that is capable of playing multiple video games with no human intervention. In 2014, the General Video Game Artificial Intelligence (GVGAI) competition framework was created and released with the purpose of providing researchers a common open-source and easy-to-use platform for testing their artificial intelligence (AI) methods with potentially infinity of games created using the video game description language (VGDL). The framework has been expanded into several tracks during the last few years to meet the demands of different research directions. The agents are required either to play multiple unknown games with or without access to game simulations, or to design new game levels or rules. This survey paper presents the VGDL, the GVGAI framework, existing tracks, and reviews the wide use of GVGAI framework in research, education, and competitions five years after its birth. A future plan of framework improvements is also described.
KW - Artificial intelligence
KW - Computational intelligence
KW - Games
KW - General Video Game AI (GVGAI)
KW - General video game playing (GVGP)
KW - Video game description language (VGDL)
UR - http://www.scopus.com/inward/record.url?scp=85073105260&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073105260&partnerID=8YFLogxK
U2 - 10.1109/TG.2019.2901021
DO - 10.1109/TG.2019.2901021
M3 - Article
AN - SCOPUS:85073105260
SN - 2475-1502
VL - 11
SP - 195
EP - 214
JO - IEEE Transactions on Games
JF - IEEE Transactions on Games
IS - 3
M1 - 2901021
ER -