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.

翻译：在迭代式设计中，确定何种创意值得制作原型是核心问题。当预期改进较高且成本较低时，应当制作原型。然而，这一决策往往十分困难，因为原型制作成本差异巨大：简单的参数调整可能仅需数秒，而硬件制造则会消耗材料与能源。这种成本不对称性可能阻碍设计者对设计空间的探索。本文提出一种扩展的成本感知贝叶斯优化方法，以应对多样化的原型制作成本。该方法基于贝叶斯优化的强大能力，仅需对采集函数进行最小程度的修改。其核心思想是利用设计者预估的成本来引导采样，从而获得更具成本效益的原型。技术评估表明，该方法在达到与成本无关基线相当效用的同时，仅需消耗约70%的成本；在严格预算限制下，其性能表现超出基线三倍。一项针对12名参与者的受试者内研究（基于真实操纵杆设计任务）同样验证了该方法的优势。这些结果表明，考虑原型制作成本能够使贝叶斯优化更适用于实际设计项目。