Markov models comprise states with probabilistic transitions. The analysis of these models is
ubiquitous and studied in, among others, reliability engineering, artificial intelligence …

This paper presents a novel method for the automated synthesis of probabilistic programs.
The starting point is a program sketch representing a finite family of finite-state Markov …

Probabilistic programs are key to deal with uncertainty in, eg, controller synthesis. They are
typically small but intricate. Their development is complex and error prone requiring …

Certifying verification algorithms not only return whether a given property holds or not, but
also provide an accompanying independently checkable certificate and a corresponding …

This paper presents counterexample-guided inductive synthesis (CEGIS) to automatically
synthesise probabilistic models. The starting point is a family of finite-stateMarkov chains …

Dealing with aleatoric uncertainty is key in many domains involving sequential decision
making, eg, planning in AI, network protocols, and symbolic program synthesis. This paper …

We present a method to find an optimal policy with respect to a reward function for a
discounted Markov decision process under general linear temporal logic (LTL) …

The ability to provide succinct information about why a property does, or does not, hold in a
given system is a key feature in the context of formal verification and model checking. It can …

We present a method to find an optimal policy with respect to a reward function for a
discounted Markov decision process under general linear temporal logic (LTL) …

Different stochastic extensions of hybrid automata have been proposed in the past, with
unclear expressivity relations between them. In previous work, we related these modelling …