  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.
