Active automata learning is a technique to automatically infer behavioral models of black-
box systems. Today's learning algorithms enable the deduction of models that describe …

We present an algorithm for active learning of deterministic timed automata with a single
clock. The algorithm is within the framework of Angluin's L^* algorithm and inspired by …

Abstract Model-based testing is a promising technique for quality assurance. In practice,
however, a model is not always present. Hence, model learning techniques attain increasing …

We present Mealy machines with a single timer (MM1Ts), a class of sufficiently expressive
models to describe the real-time behavior of many realistic applications that we can learn …

Safety is still one of the major research challenges in reinforcement learning (RL). In this
paper, we address the problem of how to avoid safety violations of RL agents during …

Abstract Automata learning is a technique for automatically inferring models of existing
systems, that enables formal verification of black-box systems. In this paper we propose a …

Automata-based modeling of hybrid and cyber-physical systems (CPS) is an important
formal abstraction amenable to algorithmic analysis of its dynamic behaviors, such as in …

Active learning of timed languages is concerned with the inference of timed automata by
observing some of the timed words in their languages. The learner can query for the …

We present an algorithm to learn a deterministic timed automaton (DTA) via membership
and equivalence queries. Our algorithm is an extension of the L* algorithm with a Myhill …

Active automata learning has been a successful technique to learn the behaviour of state-
based systems by interacting with them through queries. In this paper, we develop a …