  In this paper we introduce BO-Muse, a new approach to human-AI teaming for
the optimization of expensive black-box functions. Inspired by the intrinsic
difficulty of extracting expert knowledge and distilling it back into AI models
and by observations of human behavior in real-world experimental design, our
algorithm lets the human expert take the lead in the experimental process. The
human expert can use their domain expertise to its full potential, while the AI
plays the role of a muse, injecting novelty and searching for areas of weakness
to break the human out of over-exploitation induced by cognitive entrenchment.
With mild assumptions, we show that our algorithm converges sub-linearly, at a
rate faster than the AI or human alone. We validate our algorithm using
synthetic data and with human experts performing real-world experiments.
