The design and control of autonomous systems that operate in uncertain or adversarial
environments can be facilitated by formal modeling and analysis. Probabilistic model …

Probabilistic model checking is a powerful technique for formally verifying quantitative
properties of systems that exhibit stochastic behaviour. Such systems are found in many …

Safe exploration is a common problem in reinforcement learning (RL) that aims to prevent
agents from making disastrous decisions while exploring their environment. A family of …

We present automated techniques for the verification and control of partially observable,
probabilistic systems for both discrete and dense models of time. For the discrete-time case …

We consider partially observable Markov decision processes (POMDPs) with ω-regular
conditions specified as parity objectives. The class of ω-regular languages provides a robust …

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 …

Partially observable Markov decision processes (POMDPs) are models for sequential
decision-making under uncertainty and incomplete information. Machine learning methods …

Partially observable Markov decision processes (POMDPs) have been used as
mathematical models for sequential decision-making under uncertain and incomplete …

The verification problem in MDPs asks whether, for any policy resolving the nondeterminism,
the probability that something bad happens is bounded by some given threshold. This …

Abstract Partially-Observable Markov Decision Processes (POMDPs) are a well-known
stochastic model for sequential decision making under limited information. We consider the …