The process of developing theories and models and testing them with experiments is fundamental to the scientific method. Automating the entire scientific method then requires not only automation of the induction of theories from data, but also experimentation from design to implementation. This is the idea behind a robot scientist -- a coupled system of AI and laboratory robotics that has agency to test hypotheses with real-world experiments. In this chapter we explore some of the fundamentals of robot scientists in the philosophy of science. We also map the activities of a robot scientist to machine learning paradigms, and argue that the scientific method shares an analogy with active learning. We demonstrate these concepts using examples from previous robot scientists, and also from Genesis: a next generation robot scientist designed for research in systems biology, comprising a micro-fluidic system with 1000 computer-controlled micro-bioreactors and interpretable models based in controlled vocabularies and logic.

翻译：发展理论与模型并通过实验进行验证的过程是科学方法的基础。要实现整个科学方法的自动化，不仅需要从数据中归纳理论的自动化，还需要从实验设计到实施的全程自动化。这正是机器人科学家理念的核心——一种结合人工智能与实验室机器人技术的耦合系统，能够通过真实世界实验自主验证假说。本章从科学哲学角度探讨机器人科学家的若干基本原理，并将其活动映射至机器学习范式，论证科学方法与主动学习之间的类比关系。我们通过既有机器人科学家案例及新一代系统Genesis进行概念阐释：该系统专为系统生物学研究设计，包含配备1000个计算机控制微生物反应器的微流控平台，以及基于受控词汇与逻辑的可解释模型。