AALpy is an extensible open-source Python library providing efficient implementations of
active automata learning algorithms for deterministic, non-deterministic, and stochastic …

We present L#, a new and simple approach to active automata learning. Instead of focusing
on equivalence of observations, like the L∗ algorithm and its descendants, L# takes a …

Implementations of stateful security protocols must carefully manage the type and order of
exchanged messages and cryptographic material, by maintaining a state machine which …

Active automata learning is slowly becoming a standard tool in the toolbox of the software
engineer. As systems become ever more complex and development becomes more …

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 …

Modelling the behaviour of state-based systems can be challenging, especially when the
modeller is not entirely certain about its intended interactions with the user or the …

Abstract Model learning has gained increasing interest in recent years. It derives
behavioural models from test data of black-box systems. The main advantage offered by …

Abstract Automata learning techniques automatically generate system models from test
observations. These techniques usually fall into two categories: passive and active. Passive …

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 …