摘要翻译：
本文将机器学习和混沌数学应用于室内攀岩路线设计中。混沌变体在音乐和舞蹈中被用于巨大的优势，但这里的挑战是完全不同的，从表现开始。我们给出了一个形式化的攀岩问题转录系统，然后描述了一个变异生成器，该生成器旨在支持人类路线设置者设计新的有趣的攀岩问题。这种变异生成器被称为奇异测试版，它结合了混沌和机器学习，前者引入新奇，后者以符合攀岩风格的方式平滑过渡。这需要解析攀岩者用来描述路线和运动的特定领域自然语言，然后学习结果中的模式。我们在一个小型大学攀岩馆进行了一项试验研究，然后在一个商业攀岩馆进行了一项大型盲法研究，与经验丰富的攀岩者和专家路线设置者合作，验证了这一方法。结果表明，{\sc奇异贝塔}可以帮助人类设定者产生至少与传统方式相同的路线，在某些情况下甚至比传统方式更好的路线。
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英文标题：
《Strange Beta: An Assistance System for Indoor Rock Climbing Route
  Setting Using Chaotic Variations and Machine Learning》
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作者：
Caleb Phillips, Lee Becker, and Elizabeth Bradley
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Human-Computer Interaction        人机交互
分类描述：Covers human factors, user interfaces, and collaborative computing. Roughly includes material in ACM Subject Classes H.1.2 and all of H.5, except for H.5.1, which is more likely to have Multimedia as the primary subject area.
包括人为因素、用户界面和协作计算。大致包括ACM学科课程H.1.2和所有H.5中的材料，除了H.5.1，它更有可能以多媒体作为主要学科领域。
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  This paper applies machine learning and the mathematics of chaos to the task of designing indoor rock-climbing routes. Chaotic variation has been used to great advantage on music and dance, but the challenges here are quite different, beginning with the representation. We present a formalized system for transcribing rock climbing problems, then describe a variation generator that is designed to support human route-setters in designing new and interesting climbing problems. This variation generator, termed Strange Beta, combines chaos and machine learning, using the former to introduce novelty and the latter to smooth transitions in a manner that is consistent with the style of the climbs This entails parsing the domain-specific natural language that rock climbers use to describe routes and movement and then learning the patterns in the results. We validated this approach with a pilot study in a small university rock climbing gym, followed by a large blinded study in a commercial climbing gym, in cooperation with experienced climbers and expert route setters. The results show that {\sc Strange Beta} can help a human setter produce routes that are at least as good as, and in some cases better than, those produced in the traditional manner.
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PDF链接：
https://arxiv.org/pdf/1110.0532