Abstract Evaluation of scientific contributions can be done in many different ways. For the
various research communities working on the verification of systems (software, hardware, or …

Parametric timed automata are a powerful formalism for reasoning on concurrent real-time
systems with unknown or uncertain timing constants. In order to test the efficiency of new …

Automata learning is a technique to infer an automaton model of a black-box system via
queries to the system. In recent years it has found widespread use both in industry and …

Durch zunehmende Agilität im Entwicklungsprozess, kürzere Lebenszyklen und sich
ändernde Kunden-und Gesetzgeberanforderungen müssen Produktionssysteme …

A graph neural network is a deep learning architecture operating on graph-structured data.
While they have achieved impressive results in many application domains, their applicability …

Abstract SMPT (for Satisfiability Modulo Petri Net) is a model checker for reachability
problems in Petri nets. It started as a portfolio of methods to experiment with symbolic model …

Graph neural networks have proven their effectiveness across a wide spectrum of graph-
based tasks. Despite their successes, they share the same limitations as other deep learning …

We define a method for taking advantage of net reductions in combination with a SMT-based
model checker. We prove the correctness of this method using a new notion of equivalence …

We propose a semi-decision procedure for checking generalized reachability properties, on
generalized Petri nets, that is based on the Property Directed Reachability (PDR) method …

We propose a method for checking generalized reachability properties in Petri nets that
takes advantage of structural reductions and that can be used, transparently, as a pre …