Christopher Amato

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Frans A. OliehoekAssociate Professor at Delft University of TechnologyVerified email at tudelft.nl
Jonathan P. HowRichard C. Maclaurin Professor of Aerospace Engineering, Massachusetts Institute of TechnologyVerified email at mit.edu
Shlomo ZilbersteinProfessor of Computer Science, University of Massachusetts AmherstVerified email at cs.umass.edu
John VianVerified email at ieee.org
Shayegan OmidshafieiGoogleVerified email at google.com
Mykel J. KochenderferAssociate Professor, Stanford UniversityVerified email at stanford.edu
Jilles S. DibangoyeAssociate Professor at INSA Lyon / Inria Grenoble Rhône-AlpesVerified email at insa-lyon.fr
Nazim Kemal UreIstanbul Technical UniversityVerified email at itu.edu.tr
Miao LiuIBMVerified email at ibm.com
Girish ChowdharyAssoc. Professor of RoboticsVerified email at illinois.edu
Yuchen XiaoResearch Scientist, J.P. Morgan AI Research | Ph.D. Northeastern UniversityVerified email at northeastern.edu
George KonidarisBrownVerified email at cs.brown.edu
Leslie KaelblingVerified email at csail.mit.edu
Brett DaleyUniversity of AlbertaVerified email at ualberta.ca
Ali-akbar Agha-mohammadiNASA-JPL, CaltechVerified email at jpl.nasa.gov
Sammie KattNortheastern UniversityVerified email at husky.neu.edu
François CharpilletINRIA - LORIAVerified email at inria.fr
Matthijs T. J. SpaanDelft University of TechnologyVerified email at tudelft.nl
Shih-Yuan LiuDirector of Engineering - Planning, MotionalVerified email at motional.com
Andrea BaiseroPhD student, Northeastern UniversityVerified email at northeastern.edu

Christopher Amato

Other namesChris Amato

Associate Professor at Northeastern University

Verified email at ccs.neu.edu - Homepage

Artificial Intelligence Multi-Agent Systems Multi-Robot Systems Reinforcement Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
A concise introduction to decentralized POMDPs FA Oliehoek, C Amato Springer, 2016	779	2016
Decision making under uncertainty: theory and application MJ Kochenderfer, C Amato, G Chowdhary, JP How, HJD Reynolds, ... MIT press, 2015	664	2015
Deep Decentralized Multi-task Multi-Agent RL under Partial Observability S Omidshafiei, J Pazis, C Amato, JP How, J Vian International Conference on Machine Learning, 2681-2690, 2017	515	2017
Optimizing fixed-size stochastic controllers for POMDPs and decentralized POMDPs C Amato, DS Bernstein, S Zilberstein Autonomous Agents and Multi-Agent Systems 21 (3), 293-320, 2010	150	2010
Decentralized Control of Partially Observable Markov Decision Processes C Amato, G Chowdhary, A Geramifard, NK Ure, M Kochenderfer Proceedings of the Fifty-Second IEEE Conference on Decision and Control (CDC-13), 2013	140	2013
Policy iteration for decentralized control of Markov decision processes DS Bernstein, C Amato, EA Hansen, S Zilberstein Journal of Artificial Intelligence Research 34 (1), 89, 2009	136	2009
Optimally Solving Dec-POMDPs as Continuous-State MDPs JS Dibangoye, C Amato, O Buffet, F Charpillet Proceedings of the Twenty-Third International Joint Conference on Artificial …, 2013	131	2013
Planning for Decentralized Control of Multiple Robots Under Uncertainty C Amato, G Konidaris, G Cruz, CA Maynor, JP How, LP Kaelbling IEEE International Conference on Robotics and Automation, 2015	116	2015
Learning to Teach in Cooperative Multiagent Reinforcement Learning S Omidshafiei, DK Kim, M Liu, G Tesauro, M Riemer, C Amato, ... AAAI, 2018	113	2018
Scalable Planning and Learning for Multiagent POMDPs C Amato, FA Oliehoek AAAI Conference on Artificial Intelligence, 1995-2002, 2015	103	2015
Policy search for multi-robot coordination under uncertainty C Amato, G Konidaris, A Anders, G Cruz, JP How, LP Kaelbling The International Journal of Robotics Research 35 (14), 1760-1778, 2016	91	2016
High-level reinforcement learning in strategy games. C Amato, G Shani AAMAS 10, 75-82, 2010	86	2010
Incremental policy generation for finite-horizon DEC-POMDPs C Amato, JS Dibangoye, S Zilberstein Proc. of the 19th Int’l Conf. on Automated Planning and Scheduling, 2-9, 2009	86	2009
Planning with Macro-Actions in Decentralized POMDPs C Amato, GD Konidaris, LP Kaelbling International Conference on Autonomous Agents and Multiagent Systems, 2014	85	2014
Optimizing memory-bounded controllers for decentralized POMDPs C Amato, DS Bernstein, S Zilberstein Proceedings of the Twenty-Third Conference on Uncertainty in Artificial …, 2007	85*	2007
Decentralized control of multi-robot partially observable Markov decision processes using belief space macro-actions S Omidshafiei, AA Agha–Mohammadi, C Amato, SY Liu, JP How, J Vian The International Journal of Robotics Research 36 (2), 231-258, 2017	75	2017
Solving POMDPs using quadratically constrained linear programs C Amato, DS Bernstein, S Zilberstein Proceedings of the Twentieth International Joint Conference on Artificial …, 2007	70	2007
Achieving goals in decentralized POMDPs C Amato, S Zilberstein Proceedings of The 8th International Conference on Autonomous Agents and …, 2009	67	2009
Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning X Lyu, Y Xiao, B Daley, C Amato Autonomous Agents and Multi-Agent Systems (AAMAS), 2021	66	2021
Learning in POMDPs with Monte Carlo Tree Search S Katt, FA Oliehoek, C Amato ICML, 2017	66	2017

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