Christopher Amato

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Frans A. OliehoekAssociate Professor at Delft University of TechnologyVerificeret mail på tudelft.nl
Jonathan P. HowRichard C. Maclaurin Professor of Aerospace Engineering, Massachusetts Institute of TechnologyVerificeret mail på mit.edu
Shlomo ZilbersteinProfessor of Computer Science, University of Massachusetts AmherstVerificeret mail på cs.umass.edu
John VianVerificeret mail på ieee.org
Shayegan OmidshafieiChief Scientist at Field AI (previously: Google DeepMind, Google Research, MIT)Verificeret mail på google.com
Yuchen XiaoResearch Scientist, J.P. Morgan | Ph.D. Northeastern UniversityVerificeret mail på northeastern.edu
Mykel J. KochenderferAssociate Professor, Stanford UniversityVerificeret mail på stanford.edu
Brett DaleyUniversity of AlbertaVerificeret mail på ualberta.ca
Nazim Kemal UreIstanbul Technical UniversityVerificeret mail på itu.edu.tr
Miao LiuIBMVerificeret mail på ibm.com
Jilles S. DibangoyeAssociate Professor at University of GroningenVerificeret mail på rug.nl
Girish ChowdharyAssociate ProfessorVerificeret mail på illinois.edu
Andrea BaiseroPhD student, Northeastern UniversityVerificeret mail på northeastern.edu
George KonidarisBrownVerificeret mail på cs.brown.edu
Leslie KaelblingVerificeret mail på csail.mit.edu
Sammie KattAalto UniversityVerificeret mail på aalto.fi
Ali AghaNASA-JPL, CaltechVerificeret mail på jpl.nasa.gov
Xueguang LyuNortheastern UniversityVerificeret mail på northeastern.edu
Matthijs T. J. SpaanDelft University of TechnologyVerificeret mail på tudelft.nl
François CharpilletINRIA - LORIAVerificeret mail på inria.fr

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Christopher Amato

Andre navneChris Amato

Associate Professor at Northeastern University

Verificeret mail på ccs.neu.edu - Startside

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


Titel Sortér efter henvisninger Sortér efter årstal Sortér efter titel	Citeret af Citeret af	År
A concise introduction to decentralized POMDPs FA Oliehoek, C Amato Springer International Publishing, 2016	1398	2016
Decision making under uncertainty: theory and application MJ Kochenderfer, C Amato, G Chowdhary, JP How, HJD Reynolds, ... MIT press, 2015	839	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	679	2017
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	178	2013
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	171	2013
Learning to Teach in Cooperative Multiagent Reinforcement Learning S Omidshafiei, DK Kim, M Liu, G Tesauro, M Riemer, C Amato, ... AAAI, 2018	167	2018
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	164	2021
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	164	2010
Planning with Macro-Actions in Decentralized POMDPs C Amato, GD Konidaris, LP Kaelbling International Conference on Autonomous Agents and Multiagent Systems, 2014	163*	2014
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	142	2009
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	136	2015
Scalable Planning and Learning for Multiagent POMDPs C Amato, FA Oliehoek AAAI Conference on Artificial Intelligence, 1995-2002, 2015	132	2015
Safe Multi-Agent Reinforcement Learning via Shielding I Elsayed-Aly, S Bharadwaj, C Amato, R Ehlers, U Topcu, L Feng Autonomous Agents and Multi-Agent Systems (AAMAS), 2021	103	2021
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	99	2016
Learning in POMDPs with Monte Carlo Tree Search S Katt, FA Oliehoek, C Amato ICML, 2017	94	2017
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	93	2017
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	93	2009
Optimizing memory-bounded controllers for decentralized POMDPs C Amato, DS Bernstein, S Zilberstein Proceedings of the Twenty-Third Conference on Uncertainty in Artificial …, 2007	92*	2007
High-level reinforcement learning in strategy games. C Amato, G Shani AAMAS 10, 75-82, 2010	89	2010
Online planning for target object search in clutter under partial observability Y Xiao, S Katt, A ten Pas, S Chen, C Amato 2019 International Conference on Robotics and Automation (ICRA), 8241-8247, 2019	87	2019

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