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 …

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 …

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 …

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 …

Dealing with aleatoric uncertainty is key in many domains involving sequential decision
making, eg, planning in AI, network protocols, and symbolic program synthesis. This paper …

Belief-based programming is a probabilistic extension of the Golog programming language
family, where every action and sensing could be noisy and every test refers to the subjective …

We propose a hierarchical framework of optimal transports (OTs), namely string diagrams of
OTs. Our target problem is a safety problem on string diagrams of OTs, which requires …