The Use of AI-Robotic Systems for Scientific Discovery
Preprint, 2026
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
Författare
Alexander Gower
Göteborgs universitet
Chalmers, Data- och informationsteknik, Data Science och AI
Konstantin Korovin
Chalmers, Life sciences, Systembiologi
Daniel Brunnsåker
Chalmers, Data- och informationsteknik, Data Science och AI
Göteborgs universitet
Filip Kronström
Chalmers, Data- och informationsteknik, Data Science och AI
Göteborgs universitet
Gabriel Reder
Chalmers, Data- och informationsteknik, Data Science och AI
Göteborgs universitet
Ievgeniia Tiukova
Chalmers, Life sciences, Infrastrukturer
Ronald S. Reiserer
Vanderbilt University
Ross King
Chalmers, Data- och informationsteknik, Data Science och AI
Göteborgs universitet
Ämneskategorier (SSIF 2025)
Robotik och automation
Datavetenskap (datalogi)
DOI
10.48550/arXiv.2406.17835