Player Experience

Player experience modeling (PEM) aims to understand and predict how players interact with and feel about games, focusing on individual differences and preferences. Current research utilizes machine learning, particularly transformer models and neural networks, to analyze diverse data sources like gameplay videos, in-game events, and player behavior logs, creating player representations that capture skill, personality, and affective responses. These advancements enable more personalized game design, improved matchmaking, and a deeper understanding of player behavior, impacting both game development and the broader field of human-computer interaction.