Exploring Dynamic Difficulty Adjustment Methods for Video Games

Abstract

Maintaining player engagement is pivotal for video game success, yet achieving the optimal difficulty level that adapts to diverse player skills remains a significant challenge. Initial difficulty settings in games often fail to accommodate the evolving abilities of players, necessitating adaptive difficulty mechanisms to keep the gaming experience engaging. This study introduces a custom first-person-shooter (FPS) game to explore Dynamic Difficulty Adjustment (DDA) techniques, leveraging both performance metrics and emotional responses gathered from physiological sensors. Through a within-subjects experiment involving casual and experienced gamers, we scrutinized the effects of various DDA methods on player performance and self-reported game perceptions. Contrary to expectations, our research did not identify a singular, most effective DDA strategy. Instead, findings suggest a complex landscape where no one approach—be it performance-based, emotion-based, or a hybrid—demonstrably surpasses static difficulty settings in enhancing player engagement or game experience. Noteworthy is the data’s alignment with Flow Theory, suggesting potential for the Emotion DDA technique to foster engagement by matching challenges to player skill levels. However, the overall modest impact of DDA on performance metrics and emotional responses highlights the intricate challenge of designing adaptive difficulty that resonates with both the mechanical and emotional facets of gameplay. Our investigation contributes to the broader dialogue on adaptive game design, emphasizing the need for further research to refine DDA approaches. By advancing our understanding and methodologies, especially in emotion recognition, we aim to develop more sophisticated DDA strategies. These strategies aspire to dynamically align game challenges with individual player states, making games more accessible, engaging, and enjoyable for a wider audience.