Generating Game Levels of Diverse Behaviour Engagement

Zhang，Keyuan1,2; Bai，Jiayu1,2; Liu，Jialin1,2

10.1109/CoG51982.2022.9893697

2022

2325-4270

2325-4289

978-1-6654-5990-7

IEEE Conference on Computatonal Intelligence and Games, CIG

2022-August

167-174

21-24 Aug. 2022

Beijing, China

Recent years, there has been growing interests in experience-driven procedural level generation. Various metrics have been formulated to model player experience and help generate personalised levels. In this work, we question whether experience metrics can adapt to agents with different personas. We start by reviewing existing metrics for evaluating game levels. Then, focusing on platformer games, we design a framework integrating various agents and evaluation metrics. Experimental studies on Super Mario Bros. indicate that using the same evaluation metrics but agents with different personas can generate levels for particular persona. It implies that, for simple games, using a game-playing agent of specific player archetype as a level tester is probably all we need to generate levels of diverse behaviour engagement.

Experience-driven procedural content generation level generation personalised levels platformer games player experience

First ; Corresponding

English

National Natural Science Foundation of China[61906083];Shenzhen Fundamental Research Program[JCYJ20190809121403553];

2-s2.0-85139107216

Scopus

1.Research Institute of Trustworthy Autonomous System,Southern University of Science and Technology (SUSTech),Shenzhen,China
2.Southern University of Science and Technology (SUSTech),Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Department of Computer Science and Engineering,Shenzhen,China

Zhang，Keyuan,Bai，Jiayu,Liu，Jialin. Generating Game Levels of Diverse Behaviour Engagement[C],2022:167-174.