Bayesian optimization (BO) with Gaussian processes (GP) has become an
indispensable algorithm for black box optimization problems. Not without a dash
of irony, BO is often considered a black box itself, lacking ways to provide
reasons as to why certain parameters are proposed to be evaluated. This is
particularly relevant in human-in-the-loop applications of BO, such as in
robotics. We address this issue by proposing ShapleyBO, a framework for
interpreting BO's proposals by game-theoretic Shapley values.They quantify each
parameter's contribution to BO's acquisition function. Exploiting the linearity
of Shapley values, we are further able to identify how strongly each parameter
drives BO's exploration and exploitation for additive acquisition functions
like the confidence bound. We also show that ShapleyBO can disentangle the
contributions to exploration into those that explore aleatoric and epistemic
uncertainty. Moreover, our method gives rise to a ShapleyBO-assisted human
machine interface (HMI), allowing users to interfere with BO in case proposals
do not align with human reasoning. We demonstrate this HMI's benefits for the
use case of personalizing wearable robotic devices (assistive back exosuits) by
human-in-the-loop BO. Results suggest human-BO teams with access to ShapleyBO
can achieve lower regret than teams without.

提出了 ShapleyBO 方法来解释贝叶斯优化 (Bayesian optimization) 的提案，通过博弈论的 Shapley 值对每个参数的贡献进行量化，揭示了每个参数对贝叶斯优化的探索和开发的影响，以及探索方向中先验不确定性和后验不确定性的贡献，并实现了 ShapleyBO 辅助人机界面，以便用户在提案与人类推理不一致时干预贝叶斯优化，实验证明 ShapleyBO 可实现低遗憾度。