Probabilistic model checking is a powerful technique for formally verifying quantitative
properties of systems that exhibit stochastic behaviour. Such systems are found in many …

We propose a theoretical framework for approximate planning and learning in partially
observed systems. Our framework is based on the fundamental notion of information state …

We present automated techniques for the verification and control of partially observable,
probabilistic systems for both discrete and dense models of time. For the discrete-time case …

Recent work has addressed using formulas in linear temporal logic (LTL) as specifications
for agents planning in Markov Decision Processes (MDPs). We consider the inverse …

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

We propose a novel planning technique for satisfying tasks specified in temporal logic in
partially revealed environments. We define high-level actions derived from the environment …

Autonomous systems are often required to operate in partially observable environments.
They must reliably execute a specified objective even with incomplete information about the …

Planning robust executions under uncertainty is a fundamental challenge for building
autonomous robots. Partially Observable Markov Decision Processes (POMDPs) provide a …

POMDPs are standard models for probabilistic planning problems, where an agent interacts
with an uncertain environment. We study the problem of almost-sure reachability, where …

Recent work has considered trust-aware decision making for human-robot collaboration
(HRC) with a focus on model learning. In this paper, we are interested in enabling the HRC …