Cost-Aware Bayesian Optimization for Prototyping Interactive Devices

2026-02-02

AI Summary

This work addresses the challenge in iterative interaction design where the benefits of prototype refinement are often difficult to balance against diverse and asymmetric fabrication costs, thereby limiting effective exploration of the design space. We propose the first approach that explicitly incorporates designers’ estimated prototype costs into a Bayesian optimization framework by lightly modifying the acquisition function to enable cost-aware sampling. Our method achieves comparable utility at approximately 70% of the cost or, under strict budget constraints, delivers up to three times the performance of baseline methods. A user study with twelve participants further confirms its significant advantages in real-world design tasks, enhancing both the practicality and efficiency of Bayesian optimization for human-computer interaction prototyping.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• prototyping
• cost-aware
• Bayesian optimization
• interactive devices
• design exploration

Innovation

• Cost-Aware Bayesian Optimization
• Prototyping Cost
• Acquisition Function
• Interactive Device Design
• Design Space Exploration