Engagement-Oriented Dynamic Difficulty Adjustment

Abstract

As an emerging and lively research field, game designers are employing Dynamic Difficulty Adjustment (DDA) in Game Artificial Intelligence (Game AI) to improve player experience. Traditional DDA methods focus little on player churn, which cannot always lead to enhanced player engagement. Hence, we propose the Engagement-oriented Dynamic Difficulty Adjustment (EDDA) to meet the urgent need for a highly general and customizable solution in the game industry. EDDA directly considers players’ churn trend to ensure player engagement during gameplay. Its real-time monitoring algorithm and common parameter set are effective in quantifying and preventing player churn. We developed a prototype system integrating seven major game genres to verify the difficulty, gameplay time, and scores of the Game Engagement Questionnaire (GEQ) in multiple dimensions. EDDA has the largest mean and median of all genres in the above metrics with the highest confidence level and effect size, which demonstrates its generality and availability in improving player experience. It is fair to say that EDDA not only provides game designers with targeted player churn monitoring and intervention means, but also offers a deeper level of thinking for the generalized application of DDA and other Game AI technologies.