Over the past 27 years, quantum computing has seen a huge rise in interest from both
academia and industry. At the current rate, quantum computers are growing in size rapidly …

Safe exploration is a common problem in reinforcement learning (RL) that aims to prevent
agents from making disastrous decisions while exploring their environment. A family of …

We present for the first time a supermartingale certificate for ω-regular specifications. We
leverage the Robbins & Siegmund convergence theorem to characterize supermartingale …

Deep neural networks (DNNs) are increasingly used in safety-critical autonomous systems
as perception components processing high-dimensional image data. Formal analysis of …

Abstract Model checking undiscounted reachability and expected-reward properties on
Markov decision processes (MDPs) is key for the verification of systems that act under …

The rapid advance of deep reinforcement learning techniques enables the oversight of
safety-critical systems through the utilization of Deep Neural Networks (DNNs). This …

Many infrastructure managers have the goal to increase the capacity of their railway
infrastructure due to an increasing demand. While methods for performance calculations of …

Essential tasks for the verification of probabilistic programs include bounding expected
outcomes and proving termination in finite expected runtime. We contribute a simple yet …

Quantitative verification tools compute probabilities, expected rewards, or steady-state
values for formal models of stochastic and timed systems. Exact results often cannot be …

Rewriting logic and its implementation Maude are an expressive framework for the formal
specification and verification of software and other kinds of systems. Concurrency is …