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.

翻译：领域专家通常拥有宝贵的物理洞察力，但在诸如贝叶斯优化等全自动化决策过程中往往被忽视。本文我们将高通量（批处理）贝叶斯优化与人类学决策理论相结合，使领域专家能够影响最优实验的选择。该方法基于人类更擅长做出离散而非连续决策的假设，使专家能够影响关键早期决策。在每次迭代中，我们通过求解一个针对多个备选方案的增强型多目标优化问题，同时最大化其效用函数值之和以及协方差矩阵的行列式（等价于总变异性）。通过选取帕累托前沿拐点处的解，我们在每次迭代中返回一组具有高效用值且相互间合理区分的备选方案，由专家从中选择一项进行评估。我们证明，即使是在用户缺乏先验知识的情况下，该算法仍能恢复标准贝叶斯优化的累积遗憾。