Cyber-physical systems (CPSs) are naturally modelled as reactive systems with
nondeterministic and probabilistic dynamics. Model-based verification
techniques have proved effective in the deployment of safety-critical CPSs.
Central for a successful application of such techniques is the construction of
an accurate formal model for the system. Manual construction can be a
resource-demanding and error-prone process, thus motivating the design of
automata learning algorithms to synthesise a system model from observed system
behaviours.
This paper revisits and adapts the classic Baum-Welch algorithm for learning
Markov decision processes and Markov chains. For the case of MDPs, which
typically demand more observations, we present a model-based active learning
sampling strategy that choses examples which are most informative w.r.t.\ the
current model hypothesis. We empirically compare our approach with
state-of-the-art tools and demonstrate that the proposed active learning
procedure can significantly reduce the number of observations required to
obtain accurate models.

本文探讨应用自动机学习算法对基于模型验证的关键信息进行提取，实现对物理系统的建模，并基于主动学习和 Baum-Welch 算法对马尔可夫决策过程和马尔可夫链进行学习，最终证明了该种方法可以显著减少建模所需的观测次数。