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.
翻译:Step Mania 是流行的开放源码克隆, 是一种基于节奏的视频游戏。 正如流行游戏中常见的一样, 有很多社区设计的水平。 玩家和高级作者往往很难确定这种社区贡献的难度程度。 在这项工作中, 我们正式确定和分析在StepMania 水平上的困难预测任务, 这是一项常规回归( OR) 任务。 我们标准化了这种数据的广泛和多样化选择, 从而产生了五套数据, 其中两套是先前工作的扩展。 我们评估了许多竞争性的 OR 和非 OR 模型, 表明基于神经网络的模型大大超越了艺术水平, StepMania 级数据为深层 OR 模型提供了极好的测试床。 我们最后用用户实验来展示我们受过训练的模型优于人类标签的优势。