Decentralized partially observable Markov decision processes (Dec-POMDPs) constitute a generic and expressive framework for multiagent planning under uncertainty. However, plannin...
Frans A. Oliehoek, Shimon Whiteson, Matthijs T. J....
Sampling in the space of controls or actions is a well-established method for ensuring feasible local motion plans. However, as mobile robots advance in performance and competence ...
As users interact with an increasing array of personal computing devices, maintaining consistency of data across those devices becomes significantly more difficult. Typical soluti...
David L. Roberts, Sooraj Bhat, Charles Lee Isbell ...
Our setting is a Partially Observable Markov Decision Process with continuous state, observation and action spaces. Decisions are based on a Particle Filter for estimating the bel...
POMDPs provide a rich framework for planning and control in partially observable domains. Recent new algorithms have greatly improved the scalability of POMDPs, to the point where...