Cost-Aware Bayesian Optimization for Prototyping Interactive Devices
This work addresses the challenge of cost-effective prototyping for designers, but it is incremental as it builds on existing Bayesian optimization methods with a minimal modification.
The paper tackled the problem of efficiently selecting prototypes in iterative design by extending Bayesian optimization to account for varying prototyping costs, achieving comparable utility with about 70% of the cost and outperforming cost-agnostic baselines threefold under strict budgets.
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.