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

A classic solution technique for Markov decision processes (MDP) and stochastic games
(SG) is value iteration (VI). Due to its good practical performance, this approximative …

While value iteration (VI) is a standard solution approach to simple stochastic games
(SSGs), it suffered from the lack of a stopping criterion. Recently, several solutions have …

Probabilistic model checking is a technique for formal automated reasoning about software
or hardware systems that operate in the context of uncertainty or stochasticity. It builds upon …

Neuro-symbolic approaches to artificial intelligence, which combine neural networks with
classical symbolic techniques, are growing in prominence, necessitating formal approaches …

Entropic risk (ERisk) is an established risk measure in finance, quantifying risk by an
exponential re-weighting of rewards. We study ERisk for the first time in the context of turn …

We present version 2.0 of the Partial Exploration Tool (Pet), a tool for verification of
probabilistic systems. We extend the previous version by adding support for stochastic …

We present a general framework for applying learning algorithms and heuristical guidance
to the verification of Markov decision processes (MDPs), based on the ideas of Br\'azdil, T. et …

Identifying a Markov decision process's maximal end components is a prerequisite for
applying sound probabilistic model checking algorithms. In this paper, we present the first …

The analysis of formal models that include quantitative aspects such as timing or
probabilistic choices is performed by quantitative verification tools. Broad and mature tool …