Towards improved theoretical problems for autonomous discovery

Active learning and experimental data acquisition address the same problems, understanding a system under investigation with as few resources as possible. However there are few instances where the theoretically principled techniques in active learning or sequential experimental design have been applied to managing data acquisition in physical experimentation. Partly this is due to fundamental differences between the problems investigated within active learning and the issues faced in much physical experimentation. From a previous study we conducted into autonomous experimentation, where we developed a system capable of automatically designing experiments and proposing potential hypotheses, we aim to investigate and highlight the differences between theoretical active learning and the requirements of experimentalists. We also propose an update of the multi-armed bandit problem that provides a theoretical problem more closely aligned to that found in physical experimentation. We believe that for active learning techniques to be used more widely as tools within physical experimentation, a greater focus of research has to be placed on theoretical problems that have assumptions more closely aligned to those found commonly within physical experimentation. Assumptions such as extremely limited resources, more so than typically considered in active learning problems, along with erroneous observations or noisy oracles, should become standard features of active learning problems, as in experimentation there are rarely enough resources available to be certain about the validity of the data obtained and the quality of the hypotheses produced.