  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.
