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arXiv · 2110.03014

Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)

Giovanni Bacci·Anna Ingólfsdóttir·Kim Larsen·Raphaël Reynouard
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Abstract

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.

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Giovanni Bacci, Anna Ingólfsdóttir, Kim Larsen, Raphaël Reynouard. 2021-10-06. Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended). https://arxiv.org/abs/2110.03014

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