Human-Algorithm Collaborative Bayesian Optimization for Engineering
  Systems
Tom Savage Ehecatl Antonio del Rio Chanona
Bayesian optimization has been successfully applied throughout Chemical
Engineering for the optimization of functions that are expensive-to-evaluate,
or where gradients are not easily obtainable. However, domain experts often
possess valuable physical insights that are overlooked in fully automated
decision-making approaches, necessitating the inclusion of human input. In this
article we re-introduce the human back into the data-driven decision making
loop by outlining an approach for collaborative Bayesian optimization. Our
methodology exploits the hypothesis that humans are more efficient at making
discrete choices rather than continuous ones and enables experts to influence
critical early decisions. We apply high-throughput (batch) Bayesian
optimization alongside discrete decision theory to enable domain experts to
influence the selection of experiments. At every iteration we apply a
multi-objective approach that results in a set of alternate solutions that have
both high utility and are reasonably distinct. The expert then selects the
desired solution for evaluation from this set, allowing for the inclusion of
expert knowledge and improving accountability, whilst maintaining the
advantages of Bayesian optimization. We demonstrate our approach across a
number of applied and numerical case studies including bioprocess optimization
and reactor geometry design, demonstrating that even in the case of an
uninformed practitioner our algorithm recovers the regret of standard Bayesian
optimization. Through the inclusion of continuous expert opinion, our approach
enables faster convergence, and improved accountability for Bayesian
optimization in engineering systems.