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 …

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

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 …

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 …

In a recent paper, Belle and Levesque proposed a framework for a type of program called
belief programs, a probabilistic extension of GOLOG programs where every action and …

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

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 …