论文标题
通过快速用户适应调整动态难度
Dynamic Difficulty Adjustment via Fast User Adaptation
论文作者
论文摘要
动态难度调整（DDA）是一项适应游戏挑战以适应玩家技能的技术。它是游戏开发的关键要素，它为玩家提供了持续的动力和沉浸。但是，传统的DDA方法需要调整游戏中的参数才能为各种玩家生成级别。基于深度学习的最新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才能适应。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游戏的难度。在视频游戏环境用户测试（n = 9）中，我们提出的DDA方法的表现优于典型的基于深度学习的基线方法。
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.
