Sequential decision-making problems with multiple objectives arise naturally in practice and
pose unique challenges for research in decision-theoretic planning and learning, which has …

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

We present the probabilistic model checker Storm. Storm supports the analysis of discrete-
and continuous-time variants of both Markov chains and Markov decision processes. Storm …

This tutorial provides an introduction to probabilistic model checking, a technique for
automatically verifying quantitative properties of probabilistic systems. We focus on Markov …

Graph-based games are an important tool in computer science. They have applications in
synthesis, verification, refinement, and far beyond. We review graph-based games with …

The model-checking approach was originally formulated for verifying qualitative properties
of systems, for example safety and liveness (see Chap. 2), and subsequently extended to …

We propose an approach to synthesize control protocols for autonomous systems that
account for uncertainties and imperfections in interactions with human operators. As an …

Many real-world decision problems have multiple objectives. For example, when choosing a
medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize …

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

This paper studies optimal motion planning subject to motion and environment uncertainties.
By modeling the system as a probabilistic labeled Markov decision process (PL-MDP), the …