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 …

As bigger quantum processors with hundreds of qubits become increasingly available, the
potential for quantum computing to solve problems intractable for classical computers is …

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 …

Abstract Multiple-environment MDPs (MEMDPs) capture finite sets of MDPs that share the
states but differ in the transition dynamics. These models form a proper subclass of partially …

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 …

Probabilistic program synthesis consists in automatically creating programs generating
random values adhering to specified distributions. We consider here the family of …

The ability to provide succinct information about why a property does, or does not, hold in a
given system is a key feature in the context of formal verification and model checking. It can …

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 …

Markov Decision Processes (MDPs) model systems with uncertain transition dynamics.
Multiple-environment MDPs (MEMDPs) extend MDPs. They intuitively reflect finite sets of …