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

BO-Muse: A human expert and AI teaming framework for accelerated experimental design