This tutorial provides an introduction to probabilistic model checking, a technique for
automatically verifying quantitative properties of probabilistic systems. We focus on Markov …

We present a general framework for applying machine-learning algorithms to the verification
of Markov decision processes (MDPs). The primary goal of these techniques is to improve …

Modern software-intensive systems often interact with an environment whose behavior
changes over time, often unpredictably. The occurrence of changes may jeopardize their …

Probabilistic model checking provides formal guarantees on quantitative properties such as
reliability, performance or risk, so the accuracy of the numerical results that it returns is …

Quantitative verification techniques provide an effective means of computing performance
and reliability properties for a wide range of systems. However, the computation required …

Parametric Markov chains have been introduced as a model for families of stochastic
systems that rely on the same graph structure, but differ in the concrete transition …

The concept of quantiles is well-known in statistics, but its benefits for the formal quantitative
analysis of probabilistic systems have been noticed only recently. To compute quantiles in …

This paper introduces Farkas certificates for lower and upper bounds on minimal and
maximal reachability probabilities in Markov decision processes (MDP), which we derive …

Markov decision processes are of major interest in the planning community as well as in the
model checking community. But in spite of the similarity in the considered formal models, the …

We consider automatic synthesis of control policies for non-independent, heterogeneous
multi-agent systems with the objective of maximizing the probability of satisfying a given …