  This paper uses active learning to solve the problem of mining bounded-time
signal temporal requirements of cyber-physical systems or simply the
requirement mining problem. By utilizing robustness degree, we formulates the
requirement mining problem into two optimization problems, a parameter
synthesis problem and a falsification problem. We then propose a new active
learning algorithm called Gaussian Process Adaptive Confidence Bound (GP-ACB)
to help solving the falsification problem. We show theoretically that the
GP-ACB algorithm has a lower regret bound thus a larger convergence rate than
some existing active learning algorithms, such as GP-UCB. We finally illustrate
and apply our requirement mining algorithm on two case studies, the Ackley's
function and a real world automatic transmission model. The case studies show
that our mining algorithm with GP-ACB outperforms others, such as those based
on Nelder-Mead, by an average of 30% to 40%. Our results demonstrate that there
is a principled and efficient way of extracting requirements for complex
cyber-physical systems.
