User churn in games often arises due to inadequate game difficulty. To address this, non-player characters (NPCs) has been utilized to modulate difficulty according to individual game skill. Nevertheless, the effectiveness of solely NPC-based adjustments is limited since game difficulty is influenced by both NPCs and environmental factors. This paper introduces a novel method for dynamically tailoring game difficulty by adjusting in-game environments based on player behavior patterns in top-down shooter game. Through analysis of diverse user game play data, we find that factors within the game environment, such as the distribution of enemy characters and the arrangement of terrain, have a substantial influence on the level of difficulty. Furthermore, it has been observed that behavioral patterns of players show variations according to changes in the game environment. Using these analytical result, we devise an artificial neural network model that configures an environment that suit player behavior patterns. With the model, we figure out the user player pattern and control the difficulty dynamically by changing the environment factors. Through the experiments, we show that our method provides an appropriate level of difficulty for users to prevent user churn.

1. Introduction
2. Related Work
3. Design and Implementation
    3.1 Game Environment Element
    3.2 Data Collection
    3.3 Artificial neural network training
4. Evaluation
    4.1 Performance metric
    4.2 Game Difficulty Level Adjustment byutilizing Adaptive Environment Design
5. Conclusion
Acknowledgement

• Soo Hwan LEE(Department of Animation, College of Convergence Engineering, 20 , Hongjimun 2-gil, Jongno-gu, Seoul, Korea)
• Hanul Sung(Department of Animation, College of Convergence Engineering, 20 , Hongjimun 2-gil, Jongno-gu, Seoul, Korea) Corresponding author