Cost-Aware Bayesian Optimization for Prototyping Interactive Devices

TL;DR

This paper extends cost-aware Bayesian optimization to better handle diverse prototyping costs, guiding design exploration toward more cost-effective prototypes and improving efficiency in real-world interactive device design.

Contribution

It introduces a minimal modification to Bayesian optimization that incorporates designer-estimated costs, enhancing cost-effectiveness in prototyping decisions.

Findings

Achieved similar utility to cost-agnostic methods at 70% lower cost

Outperformed baseline threefold under strict budgets

Validated benefits through a user study with 12 participants

Abstract

Deciding which idea is worth prototyping is a central concern in iterative design. A prototype should be produced when the expected improvement is high and the cost is low. However, this is hard to decide, because costs can vary drastically: a simple parameter tweak may take seconds, while fabricating hardware consumes material and energy. Such asymmetries, can discourage a designer from exploring the design space. In this paper, we present an extension of cost-aware Bayesian optimization to account for diverse prototyping costs. The method builds on the power of Bayesian optimization and requires only a minimal modification to the acquisition function. The key idea is to use designer-estimated costs to guide sampling toward more cost-effective prototypes. In technical evaluations, the method achieved comparable utility to a cost-agnostic baseline while requiring only ${\approx}70\%$ of the cost; under strict budgets, it outperformed the baseline threefold. A within-subjects study with 12 participants in a realistic joystick design task demonstrated similar benefits. These results show that accounting for prototyping costs can make Bayesian optimization more compatible with real-world design projects.