Interpreting Multi-objective Evolutionary Algorithms via Sokoban Level Generation

被引:0
作者
Zhang, Qingquan [1 ]
Li, Yuchen [1 ]
Lin, Yuhang [1 ]
Wang, Handing [2 ]
Liu, Jialin [1 ]
机构
[1] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Guangdong Key Lab Brain Inspired Intelligent Comp, Shenzhen, Peoples R China
[2] Xidian Univ, Sch Artificial Intelligence, Xian, Peoples R China
来源
2024 IEEE CONFERENCE ON GAMES, COG 2024 | 2024年
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
Procedural Content Generation; Multi-objective Optimisation; Multi-objective Evolutionary Algorithms; Two_Arch2;
D O I
10.1109/CoG60054.2024.10645559
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents an interactive platform to interpret multi-objective evolutionary algorithms. Sokoban level generation is selected as a showcase for its widespread use in procedural content generation. By balancing the emptiness and spatial diversity of Sokoban levels, we illustrate the improved two-archive algorithm, Two_Arch2, a well-known multi-objective evolutionary algorithm. Our web-based platform integrates Two_Arch2 into an interface that visually and interactively demonstrates the evolutionary process in real-time. Designed to bridge theoretical optimisation strategies with practical game generation applications, the interface is also accessible to both researchers and beginners to multi-objective evolutionary algorithms or procedural content generation on a website. Through dynamic visualisations and interactive gameplay demonstrations, this web-based platform also has potential as an educational tool.
引用
收藏
页数:2
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