Automated planning, the problem of how an agent achieves a goal given a repertoire of
actions, is one of the foundational and most widely studied problems in the AI literature. The …

We investigate the computability of problems in probabilistic planning and partially
observable infinite-horizon Markov decision processes. The undecidability of the string …

The recent advances in computer speed and algorithms for probabilistic inference have led
to a resurgence of work on planning under uncertainty. The aim is to design AI planners for …

Planning under uncertainty is a central problem in the study of automated sequential
decision making, and has been addressed by researchers in many different fields, including …

Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling
sequential decision-making scenarios with probabilistic dynamics. They are the framework …

The recent advances in computer speed and algorithms for probabilistic inference have led
to a resurgence of work on planning under uncertainty. The aim is to design AI planners for …

We describe a new planning technique that efficiently solves probabilistic propositional
contingent planning problems by converting them into instances of stochastic satisfiability …

Planning is the model-based approach to autonomous behavior where the agent behavior is
derived automatically from a model of the actions, sensors, and goals. The main challenges …

We show that for several variations of partially observable Markov decision processes,
polynomial-time algorithms for finding control policies are unlikely to or simply don't have …

This paper is about planning in stochastic domains by means of partially observable Markov
decision processes (POMDPs). POMDPs are di cult to solve. This paper considers problems …