Player Interaction

Player interaction research explores how individuals interact within game environments, focusing on understanding and modeling these interactions for improved game design, AI development, and performance prediction. Current research utilizes various approaches, including graph neural networks to capture dynamic relationships between players, large language models to facilitate natural language interaction in game development and play, and reinforcement learning to analyze strategic decision-making in multi-agent systems. These advancements have implications for enhancing human-computer interaction, creating more engaging and realistic game experiences, and improving the performance analysis of athletes and teams in various sports.