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.

中文翻译：

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通过快速用户适应动态调整难度

动态难度调整 (DDA) 是一种调整游戏挑战以匹配玩家技能的技术。它是游戏开发中的一个关键元素，可为玩家提供持续的动力和沉浸感。然而，传统的 DDA 方法需要调整游戏中的参数来为不同的玩家生成关卡。最近基于深度学习的 DDA 方法可以缩短耗时的调整过程，但需要足够的用户演示数据进行适配。在本文中，我们提出了一种快速的用户适应方法，该方法可以通过应用元学习算法仅使用少量演示数据来调整各种玩家的游戏难度。在视频游戏环境用户测试（n=9）中，我们提出的 DDA 方法优于典型的基于深度学习的基线方法。