Quantitative Predictive Monitoring and Control for Safe Human-Machine Interaction
Journal:
arXiv
Published Date:
Dec 17, 2024
Abstract
There is a growing trend toward AI systems interacting with humans to
revolutionize a range of application domains such as healthcare and
transportation. However, unsafe human-machine interaction can lead to
catastrophic failures. We propose a novel approach that predicts future states
by accounting for the uncertainty of human interaction, monitors whether
predictions satisfy or violate safety requirements, and adapts control actions
based on the predictive monitoring results. Specifically, we develop a new
quantitative predictive monitor based on Signal Temporal Logic with Uncertainty
(STL-U) to compute a robustness degree interval, which indicates the extent to
which a sequence of uncertain predictions satisfies or violates an STL-U
requirement. We also develop a new loss function to guide the uncertainty
calibration of Bayesian deep learning and a new adaptive control method, both
of which leverage STL-U quantitative predictive monitoring results. We apply
the proposed approach to two case studies: Type 1 Diabetes management and
semi-autonomous driving. Experiments show that the proposed approach improves
safety and effectiveness in both case studies.
