Player Behavior

Research on player behavior in games focuses on understanding and modeling player actions, strategies, and interactions to improve game design, player experience, and game analytics. Current approaches leverage machine learning techniques, including transformer models, neural networks, and graph-based methods, to analyze diverse data sources such as in-game events, chat logs, and player performance metrics. This research yields insights into player personality, skill levels, and collaborative/competitive behaviors, informing the development of more engaging and balanced games, as well as enabling the detection of cheating or collusion.