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 …

Abstract Partially Observable Markov Decision Processes (POMDPs) are a powerful
framework for planning under uncertainty. They allow to model state uncertainty as a belief …

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 …

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

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 …

The synthesis problem for partially observable Markov decision processes (POMDPs) is to
compute a policy that provably adheres to one or more specifications. Yet, the general …

Partially Observable Markov Decision Processes (POMDPs) are powerful models for
sequential decision making under transition and observation uncertainties. This paper …

Markov decision processes (MDPs) provide a fundamental model for sequential decision
making under process uncertainty. A classical synthesis task is to compute for a given MDP …

Probabilistic hyperproperties specify quantitative relations between the probabilities of
reaching different target sets of states from different initial sets of states. This class of …

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 …