StepMania水平估计难度的有序回归
Ordinal Regression for Difficulty Estimation of StepMania Levels
摘要：
StepMania是一款流行的基于节奏的视频游戏的开源克隆。正如流行游戏中常见的那样，有大量的社区设计关卡。玩家和关卡作者通常很难确定这些社区贡献的难度级别。在这项工作中，我们正式和分析StepMania水平的难度预测任务作为一个有序回归（OR）任务。我们标准化了更广泛和多样化的选择，这些数据产生五个数据集，其中两个是以前工作的扩展。我们评估了许多有竞争力的OR和非OR模型，证明基于神经网络的模型显著优于最先进的模型，StepMania级别的数据为深度OR模型提供了一个很好的测试平台。最后，我们通过一个用户实验来展示我们的训练模型优于人类标记。
英文摘要：
StepMania is a popular open-source clone of a rhythm-based video game. As is common in popular games, there is a large number of community-designed levels. It is often difficult for players and level authors to determine the difficulty level of such community contributions. In this work, we formalize and analyze the difficulty prediction task on StepMania levels as an ordinal regression (OR) task. We standardize a more extensive and diverse selection of this data resulting in five data sets, two of which are extensions of previous work. We evaluate many competitive OR and non-OR models, demonstrating that neural network-based models significantly outperform the state of the art and that StepMania-level data makes for an excellent test bed for deep OR models. We conclude with a user experiment showing our trained models' superiority over human labeling.
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。
