DL-DDA --基于深度学习的动态难度调整，带有UX和游戏性约束
DL-DDA -- Deep Learning based Dynamic Difficulty Adjustment with UX and Gameplay constraints
摘要：
动态难度调整（$DDA$）是为了优化用户体验而自动改变游戏难度的过程。这是几乎所有现代游戏的重要组成部分。大多数现有的DDA方法集中在一个玩家的经验，而不看其他玩家。我们提出了一种方法，自动优化用户体验，同时考虑到其他玩家和宏观约束的游戏。该方法基于深度神经网络架构，该架构涉及在其大部分支持中具有零梯度的计数损失约束。我们提出了一种优化该损失函数的方法，并对其性能进行了理论分析。最后，我们提供了一个内部实验的实证结果，该实验是在$200,000$玩家身上进行的，并发现其表现优于游戏设计专家制作的相应的手动算法。
英文摘要：
Dynamic difficulty adjustment ($DDA$) is a process of automatically changing a game difficulty for the optimization of user experience. It is a vital part of almost any modern game. Most existing DDA approaches concentrate on the experience of a player without looking at the rest of the players. We propose a method that automatically optimizes user experience while taking into consideration other players and macro constraints imposed by the game. The method is based on deep neural network architecture that involves a count loss constraint that has zero gradients in most of its support. We suggest a method to optimize this loss function and provide theoretical analysis for its performance. Finally, we provide empirical results of an internal experiment that was done on $200,000$ players and was found to outperform the corresponding manual heuristics crafted by game design experts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
