基于循环神经网络的MoonBoard攀登路线分类与生成
Recurrent Neural Network for MoonBoard Climbing Route Classification and Generation
摘要：
对攀登路线的难度进行分类和生成新路线都是具有挑战性的。现有的机器学习模型不仅无法准确预测问题的难度，而且也无法生成合理的问题。在这项工作中，我们引入了“BetaMove”，这是我们开发的一种新的移动预处理管道，以模仿人类登山者的手部序列。预处理的移动序列，然后使用训练的路线生成器和等级预测器。通过将MoonBoard问题预处理为适当的移动序列，我们的等级预测器的准确性达到接近人类水平的性能，并且我们的路线生成器产生比以前的工作质量更好的新路线。我们证明，通过BetaMove，我们能够将人类的洞察力注入机器学习问题中，这可以成为未来关于攀爬风格分类问题的迁移学习的基础。
英文摘要：
Classifying the difficulties of climbing routes and generating new routes are both challenging. Existing machine learning models not only fail to accurately predict a problem's difficulty, but they are also unable to generate reasonable problems. In this work, we introduced "BetaMove", a new move preprocessing pipeline we developed, in order to mimic a human climber's hand sequence. The preprocessed move sequences were then used to train both a route generator and a grade predictor. By preprocessing a MoonBoard problem into a proper move sequence, the accuracy of our grade predictor reaches near human-level performance, and our route generator produces new routes of much better quality compared to previous work. We demonstrated that with BetaMove, we are able to inject human insights into the machine learning problems, and this can be the foundations for future transfer learning on climbing style classification problems.
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。
