使用动态贝叶斯优化在运动学习的存在下诱导期望效应:一个模拟研究
GilHwan Kim1, Haider A Chishty1, Fabrizio Sergi1,2
1Department of Mechanical Engineering, University of Delaware, Newark, DE 19716, USA.
bioRxiv : the preprint server for biology
|August 26, 2024
概括
动态贝叶斯优化 (DBO) 在人类循环优化 (HIL) 中通常优于标准贝叶斯优化 (BO),特别是当人类的反应因机器学习而随着时间的推移而改变时.
科学领域:
- 人与计算机的交互
- 控制系统工程 控制系统工程
- 计算神经科学是一种神经科学.
背景情况:
- 人在循环 (HIL) 优化调整了人与人互动的设备的参数,但标准的贝叶斯优化 (BO) 假定用户的响应是静态的.
- 动态贝叶斯优化 (DBO) 将时间纳入BO的内核函数,以建模不断变化的用户响应.
研究的目的:
- 调查动态贝叶斯优化 (DBO) 是否优于标准贝叶斯优化 (BO) 在人类反应表现出机器学习时用于HIL优化.
- 用基于人类运动学习模型的模拟来评估DBO对BO的有效性.
主要方法:
- 使用标准贝叶斯优化 (BO) 和动态贝叶斯优化 (DBO) 模拟的人在循环优化场景.
- 使用人类运动学习的状态空间模型来模拟参与者的反应,包括适应和使用依赖的学习.
- 在各种模拟响应动态下,统计比较了BO和DBO的趋同表现.
主要成果:
- 动态贝叶斯优化 (DBO) 始终与标准贝叶斯优化 (BO) 一样好或更好.
- 在特定的代次数后,DBO证明了对最佳输入和输出的优越趋同.
- DBO的优势早在模拟中出现,用户响应更为动态.
结论:
- 动态贝叶斯优化 (DBO) 为涉及人类运动学习的HIL优化任务提供了一个比标准贝叶斯优化 (BO) 更有效的范式.
- 当足够的代允许区分真实学习和随机可变性时,DBO的改进性能是显著的.
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