This position paper reflects on the state-of-the-art in decision-making under uncertainty. A
classical assumption is that probabilities can sufficiently capture all uncertainty in a system …

Controllers for dynamical systems that operate in safety-critical settings must account for
stochastic disturbances. Such disturbances are often modeled as process noise in a …

Markov decision processes (MDPs) are formal models commonly used in sequential
decision-making. MDPs capture the stochasticity that may arise, for instance, from imprecise …

In this paper, we study the problem of learning to satisfy temporal logic specifications with a
group of agents in an unknown environment, which may exhibit probabilistic behaviour …

Markov decision processes are widely used for planning and verification in settings that
combine controllable or adversarial choices with probabilistic behaviour. The standard …

Reactive synthesis algorithms allow automatic construction of policies to control an
environment modeled as a Markov Decision Process (MDP) that are optimal with respect to …

The difficulty of manually specifying reward functions has led to an interest in using linear
temporal logic (LTL) to express objectives for reinforcement learning (RL). However, LTL …

This letter is concerned with a data-driven technique for constructing finite Markov decision
processes (MDPs) as finite abstractions of discrete-time stochastic control systems with …

Quantitative verification tools compute probabilities, expected rewards, or steady-state
values for formal models of stochastic and timed systems. Exact results often cannot be …

Linear temporal logic (LTL) and omega-regular objectives---a superset of LTL---have seen
recent use as a way to express non-Markovian objectives in reinforcement learning. We …