TY - GEN
T1 - Automatic generation of level maps with the do what's possible representation
AU - Ashlock, Daniel
AU - Salge, Christoph
N1 - © 2019 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.
PY - 2019/9/26
Y1 - 2019/9/26
N2 - Automatic generation of level maps is a popular form of automatic content generation. In this study, a recently developed technique employing the do what's possible representation is used to create open-ended level maps. Generation of the map can continue indefinitely, yielding a highly scalable representation. A parameter study is performed to find good parameters for the evolutionary algorithm used to locate high quality map generators. Variations on the technique are presented, demonstrating its versatility, and an algorithmic variant is given that both improves performance and changes the character of maps located. The ability of the map to adapt to different regions where the map is permitted to occupy space are also tested.
AB - Automatic generation of level maps is a popular form of automatic content generation. In this study, a recently developed technique employing the do what's possible representation is used to create open-ended level maps. Generation of the map can continue indefinitely, yielding a highly scalable representation. A parameter study is performed to find good parameters for the evolutionary algorithm used to locate high quality map generators. Variations on the technique are presented, demonstrating its versatility, and an algorithmic variant is given that both improves performance and changes the character of maps located. The ability of the map to adapt to different regions where the map is permitted to occupy space are also tested.
UR - http://www.scopus.com/inward/record.url?scp=85073104977&partnerID=8YFLogxK
U2 - 10.1109/CIG.2019.8848032
DO - 10.1109/CIG.2019.8848032
M3 - Conference contribution
AN - SCOPUS:85073104977
T3 - IEEE Conference on Computatonal Intelligence and Games, CIG
BT - IEEE Conference on Games 2019, CoG 2019
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 2019 IEEE Conference on Games, CoG 2019
Y2 - 20 August 2019 through 23 August 2019
ER -