This paper marries two state-of-the-art controller synthesis methods for partially observable
Markov decision processes (POMDPs), a prominent model in sequential decision making …

The de-facto standard approach in MDP verification is based on value iteration (VI). We
propose compositional VI, a framework for model checking compositional MDPs, that …

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 …

Computing schedulers that optimize reachability probabilities in MDPs is a standard
verification task. To address scalability concerns, we focus on MDPs that are compositionally …

High-performance probabilistic model checkers like the Modest Toolset's mcsta follow the
topological ordering of an MDP's strongly connected components (SCCs) to speed up the …

Probabilistische Systeme werden von Umgebungsereignissen beeinflusst, welche mit einer
gewissen Wahrscheinlichkeit auftreten. Damit solche Systeme einwandfrei funktionieren …

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 …

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 …

Labeled continuous-time Markov chains (CTMCs) describe processes subject to random
timing and partial observability. In applications such as runtime monitoring, we must …