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 …

(PBVI) algorithm for POMDP planning. PBVI approximates an exact value iteration solution
by selecting a small set of representative belief points and then tracking the value and its …

Determining the best management actions is challenging when critical information is
missing. However, urgency and limited resources require that decisions must be made …

Partially observable Markov decision processes (POMDPs) form an attractive and principled
framework for agent planning under uncertainty. Point-based approximate techniques for …

Partially observable Markov decision processes (POMDPs) provide an elegant
mathematical framework for modeling complex decision and planning problems in …

We present a novel POMDP planning algorithm called heuristic search value iteration
(HSVI). HSVI is an anytime algorithm that returns a policy and a provable bound on its regret …

This paper explains how Partially Observable Markov Decision Processes (POMDPs) can
provide a principled mathematical framework for modelling the inherent uncertainty in …

To explore issues of developmental structure, physical embodiment, integration of multiple
sensory and motor systems, and social interaction, we have constructed an upper-torso …

For reinforcement learning in environments in which an agent has access to a reliable state
signal, methods based on the Markov decision process (MDP) have had many successes. In …

Abstract The Partially Observable Markov Decision Process has long been recognized as a
rich framework for real-world planning and control problems, especially in robotics. However …