Planning under uncertainty is critical to robotics. The partially observable Markov decision
process (POMDP) is a mathematical framework for such planning problems. POMDPs are …

Noisy sensing, imperfect control, and environment changes are defining characteristics of
many real-world robot tasks. The partially observable Markov decision process (POMDP) …

The design and control of autonomous systems that operate in uncertain or adversarial
environments can be facilitated by formal modeling and analysis. Probabilistic model …

This book presents an overview of formal decision making methods for decentralized
cooperative systems. It is aimed at graduate students and researchers in the fields of …

Decision-making for engineering systems management can be efficiently formulated using
Markov Decision Processes (MDPs) or Partially Observable MDPs (POMDPs). Typical …

The past decade has seen a significant breakthrough in research on solving partially
observable Markov decision processes (POMDPs). Where past solvers could not scale …

POMDPs provide a principled framework for planning under uncertainty, but are
computationally intractable, due to the “curse of dimensionality” and the “curse of history” …

Abstract Partially Observable Markov Decision Processes (POMDPs) provide a rich
framework for sequential decision-making under uncertainty in stochastic domains …

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

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 …