TY - GEN
T1 - Optimising Level Generators for General Video Game AI
AU - Drageset, Olve
AU - Winands, Mark H. M.
AU - Gaina, Raluca D.
AU - Perez-Liebana, Diego
N1 - Funding Information:
ACKNOWLEDGMENT This work was partially funded by the EPSRC CDT in Intelligent Games and Game Intelligence (IGGI) EP/L015846/1.
Funding Information:
This work was partially funded by the EPSRC CDT in Intelligent Games and Game Intelligence (IGGI) EP/L015846/1.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - Procedural Content Generation is an active area of research, with more interest being given recently to methods able to produce interesting content in a general context (without task-specific knowledge). To this extent, we focus on procedural level generators within the General Video Game AI framework (GVGAI). This paper proposes several topics of interest. First, a comparison baseline for GVGAI level generators, which is more flexible and robust than the existing alternatives. Second, a composite fitness evaluation function for levels based on AI playtesting. Third, a new parameterized generator, and a Meta Generator for performing parameter search on such generators are introduced. We compare the Meta Generator against random and constructive generator baselines, using the new fitness function, on 3 GVGAI games: Butterflies, Freeway and The Snowman. The Meta Generator is suggested to perform on par with or better than the baselines, depending on the game. Encouraged by these results, the Meta Generator will be submitted to the 2019 GVGAI Level Generation competition.
AB - Procedural Content Generation is an active area of research, with more interest being given recently to methods able to produce interesting content in a general context (without task-specific knowledge). To this extent, we focus on procedural level generators within the General Video Game AI framework (GVGAI). This paper proposes several topics of interest. First, a comparison baseline for GVGAI level generators, which is more flexible and robust than the existing alternatives. Second, a composite fitness evaluation function for levels based on AI playtesting. Third, a new parameterized generator, and a Meta Generator for performing parameter search on such generators are introduced. We compare the Meta Generator against random and constructive generator baselines, using the new fitness function, on 3 GVGAI games: Butterflies, Freeway and The Snowman. The Meta Generator is suggested to perform on par with or better than the baselines, depending on the game. Encouraged by these results, the Meta Generator will be submitted to the 2019 GVGAI Level Generation competition.
KW - GVGAI
KW - level generation
KW - genetic algorithm
U2 - 10.1109/CIG.2019.8847961
DO - 10.1109/CIG.2019.8847961
M3 - Conference article in proceeding
SN - 9781728118840
T3 - IEEE Conference on Computational Intelligence and Games
SP - 1
EP - 8
BT - 2019 IEEE Conference on Games (CoG)
PB - IEEE
T2 - IEEE Conference on Games (CoG) 2019
Y2 - 20 August 2019 through 23 August 2019
ER -