6 papers
Expanding Horizons of Level Diversity via Multi-objective Evolutionary Learning
Qingquan Zhang, Ziqi Wang, Yuchen Li +3
In recent years, the generation of diverse game levels has gained increasing interest, contributing to a richer and more engaging gaming experience. A number of level diversity met…
Ethical Considerations of Large Language Models in Game Playing
Qingquan Zhang, Yuchen Li, Bo Yuan +3
Large language models (LLMs) have demonstrated tremendous potential in game playing, while little attention has been paid to their ethical implications in those contexts. This work…
Measuring Diversity of Game Scenarios
Yuchen Li, Ziqi Wang, Qingquan Zhang +2
This survey comprehensively reviews the multi-dimensionality of game scenario diversity, spotlighting the innovative use of procedural content generation and other fields as corner…
Exploring Accuracy-Fairness Trade-off in Large Language Models
Qingquan Zhang, Qiqi Duan, Bo Yuan +2
Large Language Models (LLMs) have made significant strides in the field of artificial intelligence, showcasing their ability to interact with humans and influence human cognition t…
Fairness-aware Multiobjective Evolutionary Learning
Qingquan Zhang, Jialin Liu, Xin Yao
Multiobjective evolutionary learning (MOEL) has demonstrated its advantages of training fairer machine learning models considering a predefined set of conflicting objectives, inclu…
Interpreting Multi-objective Evolutionary Algorithms via Sokoban Level Generation
Qingquan Zhang, Yuchen Li, Yuhang Lin +2
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 proce…