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 …

We present a novel learning framework to obtain finite-state controllers (FSCs) for partially
observable Markov decision processes and illustrate its applicability for indefinite-horizon …

This paper marries two state-of-the-art controller synthesis methods for partially observable
Markov decision processes (POMDPs), a prominent model in sequential decision making …

We consider the problem: is the optimal expected total reward to reach a goal state in a
partially observable Markov decision process (POMDP) below a given threshold? We tackle …

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

We investigate the problem of monitoring partially observable systems with nondeterministic
and probabilistic dynamics. In such systems, every state may be associated with a risk, eg …

Strategies for partially observable Markov decision processes (POMDP) typically require
memory. One way to represent this memory is via automata. We present a method to learn …

Randomization is a powerful technique to create robust controllers, in particular in partially
observable settings. The degrees of randomization have a significant impact on the system …

Precondition inference is an important problem with many applications. Existing
precondition inference techniques for programs with arrays have limited ability to find and …