abstract
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.