Dynamic Difficulty Adjustment via Fast User Adaptation
This work addresses the challenge of personalizing game difficulty for players with limited data, though it is incremental as it builds on existing deep learning approaches.
The paper tackled the problem of dynamic difficulty adjustment in games requiring extensive user data by proposing a meta-learning method that adapts quickly with minimal demo data, achieving superior performance over a baseline in a user test with 9 participants.
Dynamic difficulty adjustment (DDA) is a technology that adapts a game's challenge to match the player's skill. It is a key element in game development that provides continuous motivation and immersion to the player. However, conventional DDA methods require tuning in-game parameters to generate the levels for various players. Recent DDA approaches based on deep learning can shorten the time-consuming tuning process, but require sufficient user demo data for adaptation. In this paper, we present a fast user adaptation method that can adjust the difficulty of the game for various players using only a small amount of demo data by applying a meta-learning algorithm. In the video game environment user test (n=9), our proposed DDA method outperformed a typical deep learning-based baseline method.