Markov chain analysis is a key technique in formal verification. A practical obstacle is that all
probabilities in Markov models need to be known. However, system quantities such as …

We study finite-state controllers (FSCs) for partially observable Markov decision processes
(POMDPs) that are provably correct with respect to given specifications. The key insight is …

This paper studies parametric Markov decision processes (pMDPs), an extension to Markov
decision processes (MDPs) where transitions probabilities are described by polynomials …

Probabilistic automata exhibit both probabilistic and non-deterministic choice. They are
therefore a powerful semantic foundation for modeling concurrent systems with random …

As I write these lines, a period of over four great years that led to this thesis ends. The TU/e
is a remarkably vivid place and it provides a nice environment for (foreign) PhD students …

We give logical characterizations of bisimulation relations for the probabilistic automata of
Segala in terms of three Hennessy-Milner style logics. The three logics characterize strong …

Markov chain analysis is a key technique in formal verification. A practical obstacle is that all
probabilities in Markov models need to be known. However, system quantities such as …

Probabilistic automata (PAs) constitute a general framework for modeling and analyzing
discrete event systems that exhibit both nondeterministic and probabilistic behavior, such as …

Labeled transition systems are typically used as behavioral models of concurrent processes.
Their labeled transitions define a one-step state-to-state reachability relation. This model …

We give a brief overview of operational models for concurrent systems that exhibit
probabilistic behavior, focussing on the interplay between probability and nondeterminism …