Assessing Video Game Balance using Autonomous Agents

被引:1
作者
Politowski, Cristiano [1 ]
Petrillo, Fabio [1 ]
ElBoussaidi, Ghizlane [1 ]
Ullmann, Gabriel C. [2 ]
Gueheneuc, Yann-Gael [2 ]
机构
[1] Ecole Technol Super, Montreal, PQ, Canada
[2] Concordia Univ, Montreal, PQ, Canada
来源
2023 IEEE/ACM 7TH INTERNATIONAL WORKSHOP ON GAMES AND SOFTWARE ENGINEERING, GAS | 2023年
关键词
game; testing; automation; deep-reinforcement-learning;
D O I
10.1109/GAS59301.2023.00011
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
As the complexity and scope of games increase, game testing, also called playtesting, becomes an essential activity to ensure the quality of video games. Yet, the manual, ad-hoc nature of game testing leaves space for automation. In this paper, we research, design, and implement an approach to supplement game testing to balance video games with autonomous agents. We evaluate our approach with two platform games. We bring a systematic way to assess if a game is balanced by (1) comparing the difficulty levels between game versions and issues with the game design, and (2) the game demands for skill or luck.
引用
收藏
页码:25 / 32
页数:8
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