Expert-guided Bayesian Optimisation for Human-in-the-loop Experimental Design of Known Systems

TL;DR

This paper introduces a Bayesian optimisation method that incorporates expert input to improve experimental design, especially in early decision stages, by balancing utility and variability through multi-objective optimisation.

Contribution

It presents a novel expert-guided Bayesian optimisation framework that leverages human discrete decision-making and multi-objective solutions to enhance experimental selection.

Findings

Algorithm recovers regret of standard Bayesian optimisation with uninformed practitioners.

Expert influence improves early decision quality in experimental design.

Method balances utility and diversity of solutions effectively.

Abstract

Domain experts often possess valuable physical insights that are overlooked in fully automated decision-making processes such as Bayesian optimisation. In this article we apply high-throughput (batch) Bayesian optimisation alongside anthropological decision theory to enable domain experts to influence the selection of optimal experiments. Our methodology exploits the hypothesis that humans are better at making discrete choices than continuous ones and enables experts to influence critical early decisions. At each iteration we solve an augmented multi-objective optimisation problem across a number of alternate solutions, maximising both the sum of their utility function values and the determinant of their covariance matrix, equivalent to their total variability. By taking the solution at the knee point of the Pareto front, we return a set of alternate solutions at each iteration that have both high utility values and are reasonably distinct, from which the expert selects one for evaluation. We demonstrate that even in the case of an uninformed practitioner, our algorithm recovers the regret of standard Bayesian optimisation.