The expanding role of reinforcement learning (RL) in safety-critical system design has
promoted ω-automata as a way to express learning requirements—often non-Markovian …

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 …

Stochastic games are a convenient formalism for modelling systems that comprise rational
agents competing or collaborating within uncertain environments. Probabilistic model …

Simple stochastic games are turn-based 2½-player zero-sum graph games with a
reachability objective. The problem is to compute the winning probabilities as well as the …

We study stochastic zero-sum games on graphs, which are prevalent tools to model decision-
making in presence of an antagonistic opponent in a random environment. In this setting, an …

We study turn-based stochastic zero-sum games with lexicographic preferences over
objectives. Stochastic games are standard models in control, verification, and synthesis of …

In this work, we propose an abstraction and refinement methodology for the controller
synthesis of discrete-time stochastic systems to enforce complex logical properties …

We study the problem of learning safe control policies that are also effective; ie, maximizing
the probability of satisfying a linear temporal logic (LTL) specification of a task, and the …

We study two-player multi-weighted reachability games played on a finite directed graph,
where an agent, called P 1, has several quantitative reachability objectives that he wants to …

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 …