# Engagement-Oriented Dynamic Difficulty Adjustment
## 1 Introduction  
With the continuous development of research in traditional Artificial Intelligence (AI) and the rapid development of the game industry, Game AI has emerged [1, 2]. The research field of Game AI covers AI within and for games [3]. The game is an interdisciplinary field where science and art merge so that it can provide a unique research environment for AI. Furthermore, games have subjective characteristics that are difficult to regulate, especially their design and experience. Specifically, the complexity and entertainment value of games make them an ideal issue for AI, and the interaction between players and games can further push the boundaries of AI methods [4, 5].  
Player experience is the lifeblood of the game industry. The experience design has made Dynamic Difficulty Adjustment (DDA) a research hotspot in recent years [6, 7, 8, 9, 10, 11, 12, 13]. DDA is a typical technology that optimizes player experience by modifying game parameters automatically. It aims to immerse players during gameplay by adapting game challenges to match player skills. Traditional difficulty curves set by game designers are not suitable for various styles of players, which leads to player churn, a severe negative signal in games [12, 13, 14, 15, 16, 17]. Therefore, churn monitoring and intervention are of great value for improving player engagement to ensure an optimized experience [18, 19]. However, few general DDA methods center on optimizing player engagement. Improving the customizability ofalgorithms and providing suitable parameter settings for different game genres in a general framework is the key to making the approach effective [3].  
Herein, we propose a new method called EDDA, which stands for Engagement-oriented Dynamic Difficulty Adjustment. EDDA directly considers players' churn trend and adjusts the game difficulty according to gameplay time in each challenge. We set four types of phases and times: sleep, active, unit, and threshold, as well as a common parameter set. The game parameters will be adjusted dynamically via the real-time monitoring of players' gameplay time in each unit time of the corresponding challenge. In addition, we introduced the fitness based on our previous work and game design experience to measure the appropriateness of difficulty settings to adapt to different players. To evaluate the performance of EDDA and its effect on player experience, we implemented a prototype system and tested it based on the evaluation metrics of participants' experiences. The results demonstrate that EDDA provides an effective approach to prevent player churn and optimize player experience for game designers.  
The main contributions of our work are threefold:  
1. A usable and available real-time monitoring algorithm is designed to improve player engagement. Game designers can formulate corresponding difficulty adjustment solutions by observing players' gameplay in different phases and times;
2. A general and scalable common parameter set is integrated based on the positive and negative correlation of game parameters and difficulty. It provides an efficient means to intervene in player churn at the player, partner, opponent, and system levels;
3. A prototype system is implemented to fully test EDDA. The fitness, gameplay time, and scores of the Game Engagement Questionnaire (GEQ) [20] are core metrics for evaluating game balance, player engagement, and player experience. Significant improvements in the metrics prove the ability of EDDA in improving player experience.  
The remainder of the paper is organized as follows: In Section 2, we introduce the flow model, player engagement, and player churn. Section 3 elaborates on formalized definitions of core concepts and the detailed design of EDDA with adaptive parameters for player churn monitoring and intervention. The application effect of EDDA and test results of the evaluation metrics are shown and analyzed in Section 4. Finally, an overall conclusion of our contributions with limitations and future work is drawn in Section 5.