贝叶斯优化
第七部分：人在回路
EN

人在回路

偏好优化既依靠人来完成，也作用于人本身。本部分汇集这些方法用于真实用户的情况：在交互式设计工具中，研究不仅测量人是否得到了好的设计，还测量人是否认为设计属于自己；在外骨骼、假肢、助听器和神经植入物中，风险直接关乎身体；在建筑、车辆、实验室和工业中，工具已经就绪，实际使用过的人却很少。

本书的核心论点正是以这些研究为证据。算法已经足够好用，制约它们的是测量（第 45.1 节）。真实回路中的反馈并不稳定；人的回答方式不同于调校方法时所用的模拟用户；优化器的提问方式还会改变人对任务的体验，因此一次会话既是估计，也是干预。对照严格的研究更常得到的是“以更少的迭代或更低的成本得到相同的结果”，而不是“更好的结果”；一些简单的替代办法，包括让人自行调节装置，有时效果也不相上下。各章报告每项研究时都给出其设计与具体数字，供读者判断每项发现能推广到什么程度。

本部分以第 19 章为前提，可以先于研究前沿各章阅读。

本部分各章

  1. 32 交互式设计与人机交互

    2017 至 2026 年间让真人进入回路的交互式设计研究及其发现：六条研究线索，能动感与性能之间一贯存在的权衡，真实会话中反馈的不稳定，关于反馈形式、群体先验、专业水平和解释的证据，以及与强基线比较的结果。

  2. 33 可穿戴机器人、健康与辅助技术

    依据人的偏好调节外骨骼、假肢、助听器、神经植入物与机器人控制器：哪些人参与了研究，测量了什么，结果如何验证，以及算法调节与人自行调节装置相比效果如何。

  3. 34 建成环境、科学与工业

    建筑、车辆、专家引导的科学研究与工业：这些领域已有偏好优化的工具和方法，真人参与却很少；本章还比较各应用领域的证据，并核查关于这些应用的常见说法能否成立。

第七部分参考文献

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