Towards Improved Theoretical Problems for Autonomous Discovery
Chris Lovell and Steve Gunn
In: International Joint Conference on Neural Networks(2012).

Abstract

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 multiarmed bandit problem that provides a theoretical problem more closely aligned to that found in physical experimentation.

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