BO as Assistant: Using Bayesian Optimization for Asynchronously Generating Design Suggestions

Many design tasks involve parameter adjustment, and designers often struggle to find desirable parameter value combinations by manipulating sliders back and forth. For such a multi-dimensional search problem, Bayesian optimization (BO) is a promising technique because of its intelligent sampling strategy; in each iteration, BO samples the most effective points considering both exploration (i.e., prioritizing unexplored regions) and exploitation (i.e., prioritizing promising regions), enabling efficient searches. However, existing BO-based design frameworks take the initiative in the design process and thus are not flexible enough for designers to freely explore the design space using their domain knowledge. In this paper, we propose a novel design framework, BO as Assistant, which enables designers to take the initiative in the design process while also benefiting from BO’s sampling strategy. The designer can manipulate sliders as usual; the system monitors the slider manipulation to automatically estimate the design goal on the fly and then asynchronously provides unexplored-yet-promising suggestions using BO’s sampling strategy. The designer can choose to use the suggestions at any time. This framework uses a novel technique to automatically extract the necessary information to run BO by observing slider manipulation without requesting additional inputs. Our framework is domain-agnostic, demonstrated by applying it to photo color enhancement, 3D shape design for personal fabrication, and procedural material design in computer graphics.