Faster, Smarter Optimization: Sensor-Augmented AI Merges Human Preferences with Real-Time Data
By Matteo Cercola, Michele Lomuscio, Dario Piga, Simone Formentin
Published on November 10, 2025| Vol. 1, Issue No. 1
Content Source
This is a curated briefing. The original article was published on cs.LG updates on arXiv.org.
Summary
This research introduces a novel approach to human-in-the-loop (HIL) optimization, specifically an extension of the GLISp algorithm, by integrating real-time sensor measurements. Traditional preference-based optimization methods often treat systems as black boxes, overlooking valuable quantitative data. The new "sensor-guided regularized GLISp" incorporates sensor information via a physics-informed hypothesis function and least-squares regularization, effectively creating a "grey-box" model that combines subjective human preferences with objective sensor feedback. This hybrid approach significantly improves convergence speed and solution quality in tasks like vehicle suspension tuning, demonstrating a more efficient and robust HIL calibration process.
Why It Matters
This advancement is crucial for AI professionals as it addresses a significant challenge in real-world AI applications: effectively combining human expertise with quantitative data. Many complex systems, from industrial design to personalized medical devices, rely on human judgment for calibration and optimization, but this can be slow and subjective. By moving beyond purely "black-box" preference learning to a "grey-box" model that integrates sensor data and domain knowledge (physics-informed hypotheses), this research paves the way for more efficient, robust, and trustworthy AI-assisted decision-making. It signifies a broader trend towards hybrid AI models that leverage the strengths of both data-driven approaches and expert knowledge, leading to faster development cycles, superior product performance, and more intuitive human-AI collaboration across diverse fields.