Planning for decentralized control of multiple robots under uncertainty
This paper presents a probabilistic framework for synthesizing control policies for general multi-robot systems that is based on decentralized partially observable Markov decision processes (Dec-POMDPs). Dec-POMDPs are a general model of decision-making where a team of agents must cooperate to optim...
Main Authors: | Amato, Christopher (Contributor), Cruz, Gabriel (Contributor), Maynor, Christopher A. (Contributor), How, Jonathan P. (Contributor), Kaelbling, Leslie P. (Contributor), Konidaris, George D. (Contributor) |
---|---|
Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Aeronautics and Astronautics (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Laboratory for Information and Decision Systems (Contributor) |
Format: | Article |
Language: | English |
Published: |
Institute of Electrical and Electronics Engineers (IEEE),
2015-12-28T00:00:56Z.
|
Subjects: | |
Online Access: | Get fulltext |
Similar Items
-
Planning with Macro-Actions in Decentralized POMDPs
by: Amato, Christopher, et al.
Published: (2016) -
Decentralized Information-Rich Planning and Hybrid Sensor Fusion for Uncertainty Reduction in Human-Robot Missions
by: Ahmed, Nisar, et al.
Published: (2013) -
Symbol acquisition for task-level planning
by: Konidaris, George, et al.
Published: (2014) -
Symbol acquisition for probabilistic high-level planning
by: Konidaris, George, et al.
Published: (2018) -
Constructing Symbolic Representations for High-Level Planning
by: Konidaris, George D., et al.
Published: (2016)