2 citations · 3 across the 6 of their papers we have counts for
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…
GVGAI-LLM: Evaluating Large Language Model Agents with Infinite Games
Yuchen Li, Cong Lin, Muhammad Umair Nasir +3
We introduce GVGAI-LLM, a video game benchmark for evaluating the reasoning and problem-solving capabilities of large language models (LLMs). Built on the General Video Game AI fra…
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…
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…
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…