智能仪器设计
Designing Intelligent Instruments
摘要：
远程科学操作需要自动化系统，这些系统可以在最少的人为干预下采取行动和作出反应。其中一个愿景是智能仪器以自动化的方式收集数据，并根据它所了解的信息决定采取哪些新的测量方法。这项创新实现了实验设计，并将其与数据分析相结合，从而完成了学习周期。这个循环是科学方法的基础。这个循环的三个基本步骤是假设生成、询问和推理。假设生成是通过人为地向仪器提供可能用于描述物理系统的可能假设的参数化集合来实现的。查询的行为是由一个查询引擎，依赖于贝叶斯自适应探索的最佳实验被选为一个最大限度地提高预期的信息增益。推理引擎使用嵌套采样算法实现，该算法为查询引擎提供了一组后验样本，从中可以估计预期的信息增益。有了这些计算结构，仪器将完善其假设，并通过测量重复学习周期，直到所研究的系统在预先指定的公差内描述。我们将展示我们对实现这一目标的第一次尝试，使用LEGO MINDSTORMS NXT机器人平台构建的智能仪器。
英文摘要：
Remote science operations require automated systems that can both act and react with minimal human intervention. One such vision is that of an intelligent instrument that collects data in an automated fashion, and based on what it learns, decides which new measurements to take. This innovation implements experimental design and unites it with data analysis in such a way that it completes the cycle of learning. This cycle is the basis of the Scientific Method. The three basic steps of this cycle are hypothesis generation, inquiry, and inference. Hypothesis generation is implemented by artificially supplying the instrument with a parameterized set of possible hypotheses that might be used to describe the physical system. The act of inquiry is handled by an inquiry engine that relies on Bayesian adaptive exploration where the optimal experiment is chosen as the one which maximizes the expected information gain. The inference engine is implemented using the nested sampling algorithm, which provides the inquiry engine with a set of posterior samples from which the expected information gain can be estimated. With these computational structures in place, the instrument will refine its hypotheses, and repeat the learning cycle by taking measurements until the system under study is described within a pre-specified tolerance. We will demonstrate our first attempts toward achieving this goal with an intelligent instrument constructed using the LEGO MINDSTORMS NXT robotics platform.
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。
