形式化学习系统

We study requirement mining for cyber-physical systems by combining signal temporal logic with active learning. The proposed GP-ACB algorithm accelerates the search for parametric temporal logic requirements by selecting informative samples, leading to faster convergence than existing Gaussian-process-based alternatives.

We develop a data-driven algorithm to construct approximate abstractions for black-box piecewise affine systems by integrating system identification, abstraction, and active sampling under temporal-logic specifications with bounded error and probability guarantees.