The process of discovery in the physical, biological and medical sciences can be painstakingly slow. Most experiments fail, and the time from initiation of research until a new advance reaches commercial production can span 20 years. This tutorial is aimed to provide experimental scientists with a foundation in the science of making decisions. Using numerical examples drawn from the experiences of the authors, the article describes the fundamental elements of any experimental learning problem. It emphasizes the important role of belief models, which include not only the best estimate of relationships provided by prior research, previous experiments and scientific expertise, but also the uncertainty in these relationships. We introduce the concept of a learning policy, and review the major categories of policies. We then introduce a policy, known as the knowledge gradient, that maximizes the value of information from each experiment. We bring out the importance of reducing uncertainty, and illustrate this process for different belief models.
From Automation to Autonomy in Smart Manufacturing: A Bayesian Optimization Framework for Modeling Multi-Objective Experimentation and Sequential Decision Making
Bangladesh University of Engineering and Technology（孟加拉工程技术大学）；West Virginia University（西弗吉尼亚大学）
Sequential Design of Genetic Circuits Under Uncertainty With Reinforcement Learning
School of Informatics, The University of Edinburgh（爱丁堡大学信息学院）；School of Informatics（信息学院）；School of Biological Sciences, The University of Edinburgh（爱丁堡大学生物科学学院）
Learning "What-if" Explanations for Sequential Decision-Making
University of Oxford（牛津大学）；The Alan Turing Institute（艾伦·图灵研究所）；University of Cambridge（剑桥大学）；Cambridge Center for AI in Medicine（剑桥人工智能医学中心）；University of California, Los Angeles（加州大学洛杉矶分校）
Autonomous battery research: Principles of heuristic operando experimentation
ISIS Neutron & Muon Source, Rutherford Appleton Laboratory（ISIS中子与穆子源、拉瑟福德-苹果顿实验室）；The Faraday Institution（法拉第机构）；Diamond Light Source, Rutherford Appleton Laboratory（Diamond光源、拉瑟福德-苹果顿实验室）；University of Cambridge, The Old Schools, Trinity Ln（剑桥大学、旧校舍、三一街）；Imperial College London, Department of Mechanical Engineering（伦敦帝国理工学院、机械工程系）
