BO-MUSE: A HUMAN EXPERT AND AI TEAMING FRAMEWORK FOR ACCELERATED EXPERIMENTAL DE-SIGN

Abstract

In this paper we introduce BO-Muse, a new approach to human-AI teaming for the optimisation of expensive blackbox functions. Inspired by the intrinsic difficulty of extracting expert knowledge and distilling it back into AI models and by observations of human behaviour 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.

1. INTRODUCTION

Bayesian Optimisation (BO) (Shahriari et al., 2015) is a popular sample-efficient optimisation technique to solve problems where the objective is expensive. It has been applied successfully in diverse areas (Greenhill et al., 2020) including material discovery (Li et al., 2017) , alloy design (Barnett et al., 2020) and molecular design (Gómez-Bombarelli et al., 2018) . However, standard BO typically operates tabula rasa, building its model of the objective from minimal priors that do not include domain-specific detail. While there has been some progress made incorporating domain-specific knowledge to accelerate BO (Li et al., 2018; Hvarfner et al., 2022) or transfer learnings from previous experiments (Shilton et al., 2017) , it remains the case that there is a significant corpus of knowledge and expertise that could potentially accelerate BO even further but which remain largely untapped due to the inherent complexities involved in knowledge extraction and exploitation. In particular, this often arises from the fact that experts tend to organise their knowledge in complex schema containing concepts, attributes and relationships (Rousseau, 2001) , making the elicitation of relevant expert knowledge, both quantitative and qualitative, a difficult task.

annex

Experimental design underpins the discovery of new materials, processes and products. However, experiments are costly, the target function is unknown and the search space unclear. To be sample-efficient, the least number of experiments must be performed. Traditionally experimental design is guided by (human) experts who uses their domain expertise and intuition to formulate an experimental design, test it, and iterate based on observations. Living beings from fungi (Watkinson et al., 2005) to ants (Pratt & Sumpter, 2006) and humans (Daw et al., 2006; Cohen et al., 2007) face a dilemma when they make these decisions: exploit the information they have, or explore to gather new information. How humans balance this dilemma has been studied in Daw et al. (2006) -examining human choices in a n-arm bandit problem, they showed that humans were highly skewed towards exploitation. Moreover, when the task requires specialised experts, cognitive entrenchment is heightened and the balance between expertise and flexibility swings further towards remaining in known paradigms.

