Publications

2022

E. Marchesini and C. Amato, “Safety-Informed Mutations for Evolutionary Deep Reinforcement Learning,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022. 
@inproceedings{GECCO22,
  title = {Safety-Informed Mutations for Evolutionary Deep Reinforcement Learning},
  booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
  author = {Marchesini, Enrico and Amato, Christopher},
  year = {2022}
}
Y. Xiao, W. Tan, and C. Amato, “Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning,” in Advances in Neural Information Processing Systems, 2022. 
@inproceedings{NeurIPS22Xiao,
  title = {Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning},
  booktitle = {Advances in Neural Information Processing Systems},
  author = {Xiao, Yuchen and Tan, Weihao and Amato, Christopher},
  year = {2022}
}
D. Melcer, S. Tripakis, and C. Amato, “Shield Decentralization for Safe Multi-Agent Reinforcement Learning,” in Advances in Neural Information Processing Systems, 2022. 
@inproceedings{NeurIPS22Melcer,
  title = {Shield Decentralization for Safe Multi-Agent Reinforcement Learning},
  booktitle = {Advances in Neural Information Processing Systems},
  author = {Melcer, Daniel and Tripakis, Stavros and Amato, Christopher},
  year = {2022}
}
H. Nguyen, A. Baisero, D. Wang, C. Amato, and R. Platt, “Leveraging Fully Observable Policies for Learning under Partial Observability,” in Proceedings of the Conference on Robot Learning, 2022. 
@inproceedings{CORL22,
  title = {Leveraging Fully Observable Policies for Learning under Partial Observability},
  booktitle = {Proceedings of the Conference on Robot Learning},
  author = {Nguyen, Hai and Baisero, Andrea and Wang, Dian and Amato, Christopher and Platt, Robert},
  year = {2022}
}
A. Baisero, B. Daley, and C. Amato, “Asymmetric DQN for Partially Observable Reinforcement Learning,” in Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2022. 
@inproceedings{UAI22,
  title = {Asymmetric {DQN} for Partially Observable Reinforcement Learning},
  booktitle = {Proceedings of the Conference on Uncertainty in Artificial Intelligence},
  author = {Baisero, Andrea and Daley, Brett and Amato, Christopher},
  year = {2022}
}
A. Baisero and C. Amato, “Unbiased Asymmetric Reinforcement Learning under Partial Observability,” in Proceedings of the Conference on Autonomous Agents and Multiagent Systems, 2022. 
@inproceedings{AAMAS22Baisero,
  title = {Unbiased Asymmetric Reinforcement Learning under Partial Observability},
  booktitle = {Proceedings of the Conference on Autonomous Agents and Multiagent Systems},
  author = {Baisero, Andrea and Amato, Christopher},
  year = {2022}
}
S. Katt, H. Nguyen, F. Oliehoek, and C. Amato, “BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs,” in Proceedings of the Conference on Autonomous Agents and Multiagent Systems, 2022. 
@inproceedings{AAMAS22Katt,
  title = {{BADDr}: Bayes-Adaptive Deep Dropout RL for POMDPs},
  booktitle = {Proceedings of the Conference on Autonomous Agents and Multiagent Systems},
  author = {Katt, Sammie and Nguyen, Hai and Oliehoek, Frans and Amato, Christopher},
  year = {2022}
}
X. Lyu, Y. Xiao, A. Baisero, and C. Amato, “A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2022. 
@inproceedings{AAAI22,
  title = {A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  author = {Lyu, Xueguang and Xiao, Yuchen and Baisero, Andrea and Amato, Christopher},
  year = {2022}
}
H. Nguyen, Z. Yang, A. Baisero, X. Ma, R. Platt, and C. Amato, “Hierarchical Reinforcement Learning under Mixed Observability,” in Proceedings of the International Workshop on the Algorithmic Foundations of Robotics, 2022. 
@inproceedings{WAFR22,
  title = {Hierarchical Reinforcement Learning under Mixed Observability},
  booktitle = {Proceedings of the International Workshop on the Algorithmic Foundations of Robotics},
  author = {Nguyen, Hai and Yang, Zhihan and Baisero, Andrea and Ma, Xiao and Platt, Robert and Amato, Christopher},
  year = {2022}
}

2021

S. Jiang and C. Amato, “Multi-Agent Reinforcement Learning with Directed Exploration and Selective Memory Reuse,” in Proceedings of the Intelligent Robotics and Multi-Agent Systems Track at the ACM Symposium on Applied Computing, 2021. 
@inproceedings{IRMAS21,
  title = {Multi-Agent Reinforcement Learning with Directed Exploration and Selective Memory Reuse},
  booktitle = {Proceedings of the Intelligent Robotics and Multi-Agent Systems Track at the ACM Symposium on Applied Computing},
  author = {Jiang, Shuo and Amato, Christopher},
  year = {2021}
}
I. Elsayed-Aly, S. Bharadwaj, C. Amato, R. Ehlers, U. Topcu, and L. Feng, “Safe Multi-Agent Reinforcement Learning via Shielding,” in Proceedings of the Conference on Autonomous Agents and Multiagent Systems, 2021. 
@inproceedings{AAMAS21Ingy,
  title = {Safe Multi-Agent Reinforcement Learning via Shielding},
  booktitle = {Proceedings of the Conference on Autonomous Agents and Multiagent Systems},
  author = {Elsayed-Aly, Ingy and Bharadwaj, Suda and Amato, Christopher and Ehlers, R{\"u}diger and Topcu, Ufuk and Feng, Lu},
  year = {2021}
}
X. Lyu, Y. Xiao, B. Daley, and C. Amato, “Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning,” in Proceedings of the Conference on Autonomous Agents and Multiagent Systems, 2021. 
@inproceedings{AAMAS21Luke,
  title = {Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning},
  booktitle = {Proceedings of the Conference on Autonomous Agents and Multiagent Systems},
  author = {Lyu, Xueguang and Xiao, Yuchen and Daley, Brett and Amato, Christopher},
  year = {2021}
}
M. Sharif, D. Erdogmus, C. Amato, and T. Padir, “End-to-End Grasping Policies for Human-in-the-Loop Robots via Deep Reinforcement Learning,” in Proceedings of the International Conference on Robotics and Automation, 2021. 
@inproceedings{ICRA21,
  title = {End-to-End Grasping Policies for Human-in-the-Loop Robots via Deep Reinforcement Learning},
  booktitle = {Proceedings of the International Conference on Robotics and Automation},
  author = {Sharif, Mohammadreza and Erdogmus, Deniz and Amato, Christopher and Padir, Taskin},
  year = {2021}
}
A. Baisero and C. Amato, “Reconciling Rewards with Predictive State Representations,” in Proceedings of the International Joint Conference on Artificial Intelligence, 2021. 
@inproceedings{IJCAI21,
  title = {Reconciling Rewards with Predictive State Representations},
  booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence},
  author = {Baisero, Andrea and Amato, Christopher},
  year = {2021}
}
Y. Xiao, X. Lyu, and C. Amato, “Local Advantage Actor-Critic for Robust Multi-Agent Deep Reinforcement Learning,” in Proceedings of the International Symposium on Multi-Robot and Multi-Agent Systems, 2021. 
@inproceedings{MRS21,
  title = {Local Advantage Actor-Critic for Robust Multi-Agent Deep Reinforcement Learning},
  booktitle = {Proceedings of the International Symposium on Multi-Robot and Multi-Agent Systems},
  author = {Xiao, Yuchen and Lyu, Xueguang and Amato, Christopher},
  year = {2021}
}

2020

Y. Xiao, J. Hoffman, T. Xia, and C. Amato, “Learning Multi-Robot Decentralized Macro-Action-Based Policies via a Centralized Q-net,” in Proceedings of the International Conference on Robotics and Automation, 2020. 
@inproceedings{ICRA20,
  title = {Learning Multi-Robot Decentralized Macro-Action-Based Policies via a Centralized Q-net},
  booktitle = {Proceedings of the International Conference on Robotics and Automation},
  author = {Xiao, Yuchen and Hoffman, Joshua and Xia, Tian and Amato, Christopher},
  year = {2020}
}
X. Lyu and C. Amato, “Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning,” in Proceedings of the Conference on Autonomous Agents and Multiagent Systems, 2020. 
@inproceedings{AAMAS20,
  title = {Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning},
  booktitle = {Proceedings of the Conference on Autonomous Agents and Multiagent Systems},
  author = {Lyu, Xueguang and Amato, Christopher},
  year = {2020}
}
R. Yehoshua and C. Amato, “Hybrid Independent Learning in Cooperative Markov Games,” in Proceedings of the International Conference on Distributed Artificial Intelligence, 2020. 
@inproceedings{DAI20,
  title = {Hybrid Independent Learning in Cooperative Markov Games},
  booktitle = {Proceedings of the International Conference on Distributed Artificial Intelligence},
  author = {Yehoshua, Roi and Amato, Christopher},
  year = {2020}
}
B. Gucsi, D. Tarapore, W. Yeoh, C. Amato, and L. Tran-Thanh, “To Ask or Not to Ask: A User Annoyance Aware Preference Elicitation Framework for Social Robots,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020. 
@inproceedings{IROS20,
  title = {To Ask or Not to Ask: A User Annoyance Aware Preference Elicitation Framework for Social Robots},
  booktitle = {Proceedings of {IEEE/RSJ} International Conference on Intelligent Robots and Systems},
  author = {Gucsi, Balint and Tarapore, Danesh and Yeoh, William and Amato, Christopher and Tran-Thanh, Long},
  year = {2020}
}
M. Sharif, D. Erdogmus, C. Amato, and T. Padir, “Towards End-to-End Control of a Robot Prosthetic Hand via Reinforcement Learning,” in Proceedings of the IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, 2020. 
@inproceedings{BioRob20,
  title = {Towards End-to-End Control of a Robot Prosthetic Hand via Reinforcement Learning},
  booktitle = {Proceedings of the {IEEE RAS/EMBS} International Conference on Biomedical Robotics and Biomechatronics},
  author = {Sharif, Mohammadreza and Erdogmus, Deniz and Amato, Christopher and Padir, Taskin},
  year = {2020}
}
H. Nguyen, B. Daley, X. Song, C. Amato, and R. Platt, “Belief-Grounded Networks for Accelerated Robot Learning under Partial Observability,” in Proceedings of the Conference on Robot Learning, 2020. 
@inproceedings{CoRL20Hai,
  title = {Belief-Grounded Networks for Accelerated Robot Learning under Partial Observability},
  booktitle = {Proceedings of the Conference on Robot Learning},
  author = {Nguyen, Hai and Daley, Brett and Song, Xinchao and Amato, Christopher and Platt, Robert},
  year = {2020}
}
C. Xu, C. Amato, and L. Wong, “Hierarchical Robot Navigation in Novel Environments using Rough 2-D Maps,” in Proceedings of the Conference on Robot Learning, 2020. 
@inproceedings{CoRL20Chen,
  title = {Hierarchical Robot Navigation in Novel Environments using Rough 2-D Maps},
  booktitle = {Proceedings of the Conference on Robot Learning},
  author = {Xu, Chengguang and Amato, Christopher and Wong, Lawson},
  year = {2020}
}

2019

B. Daley and C. Amato, “Reconciling λ-Returns with Experience Replay,” in Advances in Neural Information Processing Systems, 2019, pp. 1133–1142. 
@inproceedings{daley2019reconciling,
  title = {Reconciling $\lambda$-Returns with Experience Replay},
  author = {Daley, Brett and Amato, Christopher},
  booktitle = {Advances in Neural Information Processing Systems},
  pages = {1133--1142},
  year = {2019}
}
Y. Xiao, J. Hoffman, and C. Amato, “Macro-Action-Based Deep Multi-Agent Reinforcement Learning,” in 3rd Annual Conference on Robot Learning, 2019. 
@inproceedings{xiao_corl_2019,
  author = {Xiao, Yuchen and Hoffman, Joshua and Amato, Christopher},
  title = {Macro-Action-Based Deep Multi-Agent Reinforcement Learning},
  booktitle = {3rd Annual Conference on Robot Learning},
  year = {2019}
}
S. Omidshafiei et al., “Learning to Teach in Cooperative Multiagent Reinforcement Learning,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2019. 
@inproceedings{AAAI19,
  author = {Omidshafiei, Shayegan and Kim, Dong-Ki and Liu, Miao and Tesauro, Gerald and Riemer, Matthew and Amato, Christopher and Campbell, Murray and How, Jonathan},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  title = {Learning to Teach in Cooperative Multiagent Reinforcement Learning},
  year = {2019}
}
C. Amato, G. Konidaris, J. P. How, and L. P. Kaelbling, “Modeling and Planning with Macro-Actions in Decentralized POMDPs,” Journal of Artificial Intelligence Research, vol. 64, pp. 817–859, 2019. 
@article{JAIR19,
  author = {Amato, Christopher and Konidaris, George and How, Jonathan P. and Kaelbling, Leslie P.},
  title = {Modeling and Planning with Macro-Actions in Decentralized {POMDPs}},
  journal = {Journal of Artificial Intelligence Research},
  year = {2019},
  volume = {64},
  pages = {817-859}
}
Y. Xiao, S. Katt, A. ten Pas, S. Chen, and C. Amato, “Online Planning for Target Object Search in Clutter under Partial Observability.,” in Proceedings of the International Conference on Robotics and Automation, 2019. 
@inproceedings{ICRA19,
  title = {Online Planning for Target Object Search in Clutter under Partial Observability.},
  booktitle = {Proceedings of the International Conference on Robotics and Automation},
  author = {Xiao, Yuchen and Katt, Sammie and ten Pas, Andreas and Chen, Shengjian and Amato, Christopher},
  year = {2019}
}
S. Katt, F. Oliehoek, and C. Amato, “Bayesian Reinforcement Learning in Factored POMDPs,” in Proceedings of the Conference on Autonomous Agents and Multiagent Systems, 2019. 
@inproceedings{katt2018bayesian,
  author = {Katt, Sammie and Oliehoek, Frans and Amato, Christopher},
  title = {Bayesian Reinforcement Learning in Factored POMDPs},
  booktitle = {Proceedings of the Conference on Autonomous Agents and Multiagent Systems},
  year = {2019}
}

2018

Z. Chen et al., “The Art of Drafting: A Team-Oriented Hero Recommendation System for Multiplayer Online Battle Arena Games,” in Proceedings of the ACM Conference on Recommender Systems, 2018. 
@inproceedings{Recsys18,
  title = {The Art of Drafting: A Team-Oriented Hero Recommendation System for Multiplayer Online Battle Arena Games},
  booktitle = {Proceedings of the ACM Conference on Recommender Systems},
  author = {Chen, Zhengxing and Nguyen, Truong-Huy D. and Xu, Yuyu and Amato, Christopher and Cooper, Seth and Sun, Yizhou and El-Nasr, Magy Seif},
  year = {2018}
}
Z. Chen, C. Amato, T.-H. D. Nguyen, S. Cooper, Y. Sun, and M. S. El-Nasr, “Q-DeckRec: A Fast Deck Recommendation System for Collectible Card Games.,” in Proceedings of the IEEE Conference on Computational Intelligence and Games, 2018. 
@inproceedings{CIG18,
  title = {{Q-DeckRec}: A Fast Deck Recommendation System for Collectible Card Games.},
  booktitle = {Proceedings of the IEEE Conference on Computational Intelligence and Games},
  author = {Chen, Zhengxing and Amato, Christopher and Nguyen, Truong-Huy D. and Cooper, Seth and Sun, Yizhou and El-Nasr, Magy Seif},
  year = {2018}
}
C. Amato, “Decision-Making Under Uncertainty in Multi-Agent and Multi-Robot Systems: Planning and Learning,” in Proceedings of the International Joint Conference on Artificial Intelligence, 2018. 
@inproceedings{IJCAI18,
  title = {Decision-Making Under Uncertainty in Multi-Agent and Multi-Robot Systems: Planning and Learning},
  booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence},
  author = {Amato, Christopher},
  year = {2018}
}
T. N. Hoang, Y. Xiao, K. Sivakumar, C. Amato, and J. How, “Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems,” in Proceedings of the International Conference on Robotics and Automation, 2018. 
@inproceedings{hoang_near-optimal_2017,
  title = {Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems},
  booktitle = {Proceedings of the International Conference on Robotics and Automation},
  author = {Hoang, Trong Nghia and Xiao, Yuchen and Sivakumar, Kavinayan and Amato, Christopher and How, Jonathan},
  year = {2018}
}

2017

S. Katt, F. A. Oliehoek, and C. Amato, “Learning in POMDPs with Monte Carlo Tree Search,” in Proceedings of the International Conference on Machine Learning, 2017, pp. 1819–1827. 
@inproceedings{pmlr-v70-katt17a,
  title = {Learning in {POMDP}s with {M}onte {C}arlo Tree Search},
  author = {Katt, Sammie and Oliehoek, Frans A. and Amato, Christopher},
  booktitle = {Proceedings of the International Conference on Machine Learning},
  pages = {1819--1827},
  year = {2017}
}
S. Omidshafiei, A.-akbar Agha-mohammadi, C. Amato, and J. P. How, “Decentralized Control of Multi-Robot Partially Observable Markov Decision Processes using Belief Space Macro-actions,” The International Journal of Robotics Research, vol. 36, no. 2, pp. 231–258, 2017. 
@article{IJRR17DecPOSMDP,
  author = {Omidshafiei, Shayegan and Agha-mohammadi, {Ali-akbar} and Amato, Christopher and How, Jonathan P.},
  journal = {The International Journal of Robotics Research},
  title = {Decentralized Control of Multi-Robot Partially Observable Markov Decision Processes using Belief Space Macro-actions},
  volume = {36},
  number = {2},
  pages = {231--258},
  year = {2017}
}
C. Amato, G. D. Konidaris, A. Anders, G. Cruz, J. P. How, and L. P. Kaelbling, “Policy Search for Multi-Robot Coordination under Uncertainty,” The International Journal of Robotics Research, vol. 35, no. 14, pp. 1760–1778, 2017. 
@article{IJRR17MacDec,
  author = {Amato, Christopher and Konidaris, George D. and Anders, Ariel and Cruz, Gabriel and How, Jonathan P. and Kaelbling, Leslie P.},
  journal = {The International Journal of Robotics Research},
  title = {Policy Search for Multi-Robot Coordination under Uncertainty},
  volume = {35},
  number = {14},
  pages = {1760--1778},
  year = {2017}
}
S. Omidshafiei, C. Amato, M. Liu, J. P. How, and J. Vian, “Scalable Accelerated Decentralized Multi-Robot Policy Search in Continuous Observation Spaces,” in Proceedings of the International Conference on Robotics and Automation, 2017, pp. 863–870. 
@inproceedings{ICRA17Continuous,
  author = {Omidshafiei, Shayegan and Amato, Christopher and Liu, Miao and How, Jonathan P. and Vian, John},
  booktitle = {Proceedings of the International Conference on Robotics and Automation},
  title = {Scalable Accelerated Decentralized Multi-Robot Policy Search in Continuous Observation Spaces},
  pages = {863-870},
  year = {2017}
}
S. Omidshafiei et al., “Semantic-level Decentralized Multi-Robot Decision-Making using Probabilistic Macro-Observations,” in Proceedings of the International Conference on Robotics and Automation, 2017, pp. 871–878. 
@inproceedings{ICRA17MacroObs,
  author = {Omidshafiei, Shayegan and Liu, Shih-Yuan and Everett, Michael and Lopez, Brett and Amato, Christopher and Liu, Miao and How, Jonathan P. and Vian., John},
  booktitle = {Proceedings of the International Conference on Robotics and Automation},
  title = {Semantic-level Decentralized Multi-Robot Decision-Making using Probabilistic Macro-Observations},
  pages = {871-878},
  year = {2017}
}

2016

J. S. Dibangoye, C. Amato, O. Buffet, and F. Charpillet, “Optimally Solving Dec-POMDPs as Continuous-State MDPs,” Journal of Artificial Intelligence Research, vol. 55, pp. 443–497, 2016. 
@article{JAIR16,
  author = {Dibangoye, Jilles S. and Amato, Christopher and Buffet, Olivier and Charpillet, Fran\c{c}ois},
  title = {Optimally Solving {Dec-POMDPs} as Continuous-State {MDPs}},
  journal = {Journal of Artificial Intelligence Research},
  year = {2016},
  volume = {55},
  pages = {443-497}
}
M. Liu, C. Amato, E. Anesta, J. D. Griffith, and J. P. How, “Learning for Decentralized Control of Multiagent Systems in Large Partially Observable Stochastic Environments,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2016, pp. 2523–2529. 
@inproceedings{AAAI16,
  author = {Liu, Miao and Amato, Christopher and Anesta, Emily and Griffith, J. Daniel and How, Jonathan P.},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  title = {Learning for Decentralized Control of Multiagent Systems in Large Partially Observable Stochastic Environments},
  pages = {2523--2529},
  year = {2016}
}
S. Omidshafiei, A.-akbar Agha-mohammadi, C. Amato, S.-Y. Liu, J. P. How, and J. Vian, “Graph-based Cross Entropy Method for Solving Multi-Robot Decentralized POMDPs,” in Proceedings of the International Conference on Robotics and Automation, 2016. 
@inproceedings{ICRA16,
  author = {Omidshafiei, Shayegan and Agha-mohammadi, {Ali-akbar} and Amato, Christopher and Liu, Shih-Yuan and How, Jonathan P. and Vian, John},
  booktitle = {Proceedings of the International Conference on Robotics and Automation},
  title = {Graph-based Cross Entropy Method for Solving Multi-Robot Decentralized {POMDPs}},
  year = {2016}
}
F. A. Oliehoek and C. Amato, A Concise Introduction to Decentralized POMDPs. Springer, 2016. 
@book{Book16,
  title = {A Concise Introduction to Decentralized POMDPs},
  author = {Oliehoek, Frans A. and Amato, Christopher},
  publisher = {Springer},
  year = {2016}
}

2015

C. Amato and F. A. Oliehoek, “Scalable Planning and Learning for Multiagent POMDPs,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2015. 
@inproceedings{AAAI15,
  author = {Amato, Christopher and Oliehoek, Frans A.},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  title = {Scalable Planning and Learning for Multiagent {POMDPs}},
  year = {2015}
}
C. Amato, G. D. Konidaris, G. Cruz, C. A. Maynor, J. P. How, and L. P. Kaelbling, “Planning for Decentralized Control of Multiple Robots Under Uncertainty,” in Proceedings of the International Conference on Robotics and Automation, 2015, pp. 1241–1248. 
@inproceedings{ICRA15MacDec,
  author = {Amato, Christopher and Konidaris, George D. and Cruz, Gabriel and Maynor, Christopher A. and How, Jonathan P. and Kaelbling, Leslie P.},
  booktitle = {Proceedings of the International Conference on Robotics and Automation},
  title = {Planning for Decentralized Control of Multiple Robots Under Uncertainty},
  pages = {1241--1248},
  year = {2015}
}
S. Omidshafiei, A.-akbar Agha-mohammadi, C. Amato, and J. P. How, “Decentralized Control of Partially Observable Markov Decision Processes using Belief Space Macro-actions,” in Proceedings of the International Conference on Robotics and Automation, 2015, pp. 5962–5969. 
@inproceedings{ICRA15DecPOSMDP,
  author = {Omidshafiei, Shayegan and Agha-mohammadi, {Ali-akbar} and Amato, Christopher and How, Jonathan P.},
  booktitle = {Proceedings of the International Conference on Robotics and Automation},
  title = {Decentralized Control of Partially Observable {Markov} Decision Processes using Belief Space Macro-actions},
  pages = {5962--5969},
  year = {2015}
}
M. Liu, C. Amato, X. Liao, L. Carin, and J. P. How, “Stick-Breaking Policy Learning in Dec-POMDPs,” in Proceedings of the International Joint Conference on Artificial Intelligence, 2015, pp. 2011–2017. 
@inproceedings{IJCAI15,
  author = {Liu, Miao and Amato, Christopher and Liao, Xuejun and Carin, Lawrence and How, Jonathan P.},
  booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence},
  title = {Stick-Breaking Policy Learning in {Dec-POMDPs}},
  pages = {2011--2017},
  year = {2015}
}
C. Amato, G. D. Konidaris, A. Anders, G. Cruz, J. P. How, and L. P. Kaelbling, “Policy Search for Multi-Robot Coordination under Uncertainty,” in Robotics: Science and Systems, 2015. 
@inproceedings{RSS15,
  author = {Amato, Christopher and Konidaris, George D. and Anders, Ariel and Cruz, Gabriel and How, Jonathan P. and Kaelbling, Leslie P.},
  booktitle = {Robotics:  Science and Systems},
  title = {Policy Search for Multi-Robot Coordination under Uncertainty},
  year = {2015}
}
C. Amato, “Cooperative Decision Making,” in Decision Making Under Uncertainty: Theory and Application, M. J. Kochenderfer, Ed. MIT Press, 2015. 
@incollection{Bookchapter15,
  author = {Amato, Christopher},
  title = {Cooperative Decision Making},
  editor = {Kochenderfer, Mykel J.},
  booktitle = {Decision Making Under Uncertainty: Theory and Application},
  publisher = {MIT Press},
  optaddress = {Cambridge, MA},
  year = {2015}
}

2014

F. A. Oliehoek and C. Amato, “Best Response Bayesian Reinforcement Learning for Multiagent Systems with State Uncertainty,” in Proceedings of the Workshop on Multi-Agent Sequential Decision Making in Uncertain Domains (MSDM) at AAMAS, 2014. 
@inproceedings{Oliehoek14MSDM,
  author = {A.\ Oliehoek, Frans and Amato, Christopher},
  title = {Best Response {Bayesian} Reinforcement Learning for Multiagent Systems with State Uncertainty},
  booktitle = {Proceedings of the Workshop on Multi-Agent Sequential Decision Making in Uncertain Domains ({MSDM}) at {AAMAS}},
  year = {2014}
}
J. S. Dibangoye, C. Amato, O. Buffet, and F. Charpillet, “Exploiting Separability in Multi-Agent Planning with Continuous-State MDPs,” in Proceedings of the Conference on Autonomous Agents and Multiagent Systems, 2014. 
@inproceedings{AAMAS14DABC,
  author = {Dibangoye, Jilles S. and Amato, Christopher and Buffet, Olivier and Charpillet, Fran\c{c}ois},
  title = {Exploiting Separability in Multi-Agent Planning with Continuous-State {MDPs}},
  booktitle = {Proceedings of the Conference on Autonomous Agents and Multiagent Systems},
  year = {2014},
  optaddress = {Paris, France}
}
C. Amato, G. D. Konidaris, and L. P. Kaelbling, “Planning with Macro-Actions in Decentralized POMDPs,” in Proceedings of the Conference on Autonomous Agents and Multiagent Systems, 2014. 
@inproceedings{AAMAS14AKK,
  author = {Amato, Christopher and Konidaris, George D. and Kaelbling, Leslie P.},
  title = {Planning with Macro-Actions in Decentralized {POMDPs}},
  booktitle = {Proceedings of the Conference on Autonomous Agents and Multiagent Systems},
  year = {2014},
  optaddress = {Paris, France}
}

2013

J. S. Dibangoye, C. Amato, A. Doniec, and F. Charpillet, “Producing Efficient Error-bounded Solutions for Transition Independent Decentralized MDPs,” in Proceedings of the Conference on Autonomous Agents and Multiagent Systems, Saint Paul, MN, 2013. 
@inproceedings{AAMAS13,
  author = {Dibangoye, Jilles S. and Amato, Christopher and Doniec, Arnaud and Charpillet, Fran\c{c}ois},
  title = {Producing Efficient Error-bounded Solutions for Transition Independent Decentralized {MDPs}},
  booktitle = {Proceedings of the Conference on Autonomous Agents and Multiagent Systems},
  year = {2013},
  address = {Saint Paul, MN}
}
J. S. Dibangoye, C. Amato, O. Buffet, and F. Charpillet, “Optimally Solving Dec-POMDPs as Continuous-State MDPs,” in Proceedings of the International Joint Conference on Artificial Intelligence, Beijing, China, 2013. 
@inproceedings{IJCAI13,
  author = {Dibangoye, Jilles S. and Amato, Christopher and Buffet, Olivier and Charpillet, Fran\c{c}ois},
  title = {Optimally Solving {Dec-POMDPs} as Continuous-State {MDPs}},
  booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence},
  year = {2013},
  address = {Beijing, China}
}
F. A. Oliehoek, M. T. J. Spaan, C. Amato, and S. Whiteson, “Incremental Clustering and Expansion for Faster Optimal Planning in Dec-POMDPs,” Journal of Artificial Intelligence Research, vol. 46, pp. 449–509, 2013. 
@article{JAIR13,
  author = {Oliehoek, Frans A. and Spaan, Matthijs T. J. and Amato, Christopher and Whiteson, Shimon},
  title = {Incremental Clustering and Expansion for Faster Optimal Planning in {Dec-POMDPs}},
  journal = {Journal of Artificial Intelligence Research},
  volume = {46},
  pages = {449-509},
  year = {2013}
}
C. Amato and F. A. Oliehoek, “Bayesian Reinforcement Learning for Multiagent Systems with State Uncertainty,” in Proceedings of the Workshop on Multi-Agent Sequential Decision Making in Uncertain Domains (MSDM) at AAMAS, 2013, pp. 76–83. 
@inproceedings{MSDM13,
  author = {Amato, Christopher and Oliehoek, Frans A.},
  booktitle = {Proceedings of the Workshop on Multi-Agent Sequential Decision Making in Uncertain Domains (MSDM) at AAMAS},
  title = {Bayesian Reinforcement Learning for Multiagent Systems with State Uncertainty},
  year = {2013},
  pages = {76--83},
  note = {}
}
C. Amato, G. Chowdhary, A. Geramifard, N. K. Ure, and M. J. Kochenderfer, “Decentralized Control of Partially Observable Markov Decision Processes,” in Proceedings of the IEEE Conference on Decision and Control, 2013, pp. 2398–2405. 
@inproceedings{CDC13,
  author = {Amato, Christopher and Chowdhary, Girish and Geramifard, Alborz and Ure, Nazim Kemal and Kochenderfer, Mykel J.},
  booktitle = {Proceedings of the IEEE Conference on Decision and Control},
  title = {Decentralized Control of Partially Observable {M}arkov Decision Processes},
  optaddress = {Florence, Italy},
  pages = {2398--2405},
  year = {2013}
}

2012

J. S. Dibangoye, C. Amato, and A. Doniec, “Scaling Up Decentralized MDPs Through Heuristic Search,” in Proceedings of the Conference on Uncertainty in Artificial Intelligence, Catalina Island, CA, 2012. 
@inproceedings{UAI12,
  author = {Dibangoye, Jilles S. and Amato, Christopher and Doniec, Arnaud},
  title = {Scaling Up Decentralized {MDPs} Through Heuristic Search},
  booktitle = {Proceedings of the Conference on Uncertainty in Artificial Intelligence},
  year = {2012},
  address = {Catalina Island, CA}
}
K. Kapoor, C. Amato, N. Srivastava, and P. Schrater, “Using POMDPs to Control an Accuracy-Processing Time Tradeoff in Video Surveillance,” in Proceedings of the Annual Conference on Innovative Applications of Artificial Intelligence, Toronto, Canada, 2012. 
@inproceedings{IAAI12,
  author = {Kapoor, Komal and Amato, Christopher and Srivastava, Nisheeth and Schrater, Paul},
  title = {Using {POMDPs} to Control an Accuracy-Processing Time Tradeoff in Video Surveillance},
  booktitle = {Proceedings of the Annual Conference on Innovative Applications of Artificial Intelligence},
  year = {2012},
  address = {Toronto, Canada}
}
C. Amato and E. Brunskill, “Diagnose and Decide: An Optimal Bayesian Approach,” in Proceedings of the Workshop on Bayesian Optimization and Decision Making at NIPS, Lake Tahoe, Nevada, 2012. 
@inproceedings{NIPSworkshop12,
  author = {Amato, Christopher and Brunskill, Emma},
  title = {Diagnose and Decide: An Optimal {Bayesian} Approach},
  booktitle = {Proceedings of the Workshop on Bayesian Optimization and Decision Making at {NIPS}},
  address = {Lake Tahoe, Nevada},
  year = {2012}
}

2011

F. A. Oliehoek, M. T. J. Spaan, and C. Amato, “Scaling Up Optimal Heuristic Search in Dec-POMDPs via Incremental Expansion,” in Proceedings of the International Joint Conference on Artificial Intelligence, Barcelona, Spain, 2011. 
@inproceedings{IJCAI11,
  author = {Oliehoek, Frans A. and Spaan, Matthijs T. J. and Amato, Christopher},
  title = {Scaling Up Optimal Heuristic Search in {Dec-POMDPs} via Incremental Expansion},
  booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence},
  address = {Barcelona, Spain},
  year = {2011}
}
P. Varakantham, N. Schurr, A. Carlin, and C. Amato, “Decision Support in Organizations: A Case for OrgPOMDPs,” in Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology, Lyon, France, 2011. 
@inproceedings{IAT11,
  author = {Varakantham, Pradeep and Schurr, Nathan and Carlin, Alan and Amato, Christopher},
  title = {Decision Support in Organizations: A Case for {OrgPOMDPs}},
  booktitle = {Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology},
  address = {Lyon, France},
  year = {2011}
}

2010

C. Amato and G. Shani, “High-level Reinforcement Learning in Strategy Games,” in Proceedings of the Conference on Autonomous Agents and Multiagent Systems, Toronto, Canada, 2010. 
@inproceedings{AAMAS10AS,
  author = {Amato, Christopher and Shani, Guy},
  title = {High-level Reinforcement Learning in Strategy Games},
  booktitle = {Proceedings of the Conference on Autonomous Agents and Multiagent Systems},
  address = {Toronto, Canada},
  year = {2010}
}
F. A. Oliehoek, M. T. J. Spaan, J. S. Dibangoye, and C. Amato, “Solving Identical Payoff Bayesian Games with Heuristic Search,” in Proceedings of the Conference on Autonomous Agents and Multiagent Systems, Toronto, Canada, 2010. 
@inproceedings{AAMAS10OSDA,
  author = {Oliehoek, Frans A. and Spaan, Matthijs T. J. and Dibangoye, Jilles S. and Amato, Christopher},
  title = {Solving Identical Payoff Bayesian Games with Heuristic Search},
  booktitle = {Proceedings of the Conference on Autonomous Agents and Multiagent Systems},
  address = {Toronto, Canada},
  year = {2010}
}
C. Amato, B. Bonet, and S. Zilberstein, “Finite-State Controllers Based on Mealy Machines for Centralized and Decentralized POMDPs,” in Proceedings of the AAAI Conference on Artificial Intelligence, Atlanta, GA, 2010, pp. 1052–1058. 
@inproceedings{AAAI10,
  author = {Amato, Christopher and Bonet, Blai and Zilberstein, Shlomo},
  title = {Finite-State Controllers Based on {Mealy} Machines for Centralized and Decentralized {POMDPs}},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  pages = {1052--1058},
  address = {Atlanta, GA},
  year = {2010}
}
C. Amato, “Increasing Scalability in Algorithms for Centralized and Decentralized Partially Observable Markov Decision Processes: Efficient Decision-Making and Coordination in Uncertain Environments,” University of Massachusetts, Department of Computer Science, Amherst, MA, UM-CS-PhD-2010-005, 2010. 
@techreport{ThesisTech,
  author = {Amato, Christopher},
  title = {Increasing Scalability in Algorithms for Centralized and Decentralized Partially Observable {Markov} Decision Processes: Efficient Decision-Making and Coordination in Uncertain Environments},
  year = {2010},
  institution = {University of Massachusetts, Department of Computer Science},
  number = {UM-CS-PhD-2010-005},
  address = {Amherst, MA}
}

2009

C. Amato and S. Zilberstein, “Achieving Goals in Decentralized POMDPs,” in Proceedings of the Conference on Autonomous Agents and Multiagent Systems, Budapest, Hungary, 2009, pp. 593–600. 
@inproceedings{AAMAS09,
  author = {Amato, Christopher and Zilberstein, Shlomo},
  title = {Achieving Goals in Decentralized {POMDPs}},
  booktitle = {Proceedings of the Conference on Autonomous Agents and Multiagent Systems},
  address = {Budapest, Hungary},
  year = {2009},
  pages = {593-600}
}
C. Amato, J. S. Dibangoye, and S. Zilberstein, “Incremental Policy Generation for Finite-Horizon DEC-POMDPs,” in Proceedings of the International Conference on Automated Planning and Scheduling, Thessaloniki, Greece, 2009, pp. 2–9. 
@inproceedings{ICAPS09,
  author = {Amato, Christopher and Dibangoye, Jilles S. and Zilberstein, Shlomo},
  title = {Incremental Policy Generation for Finite-Horizon {DEC-POMDPs}},
  booktitle = {Proceedings of the International Conference on Automated Planning and Scheduling},
  address = {Thessaloniki, Greece},
  year = {2009},
  pages = {2-9}
}
C. Amato, D. S. Bernstein, and S. Zilberstein, “Optimizing fixed-size stochastic controllers for POMDPs and decentralized POMDPs,” Journal of Autonomous Agents and Multi-Agent Systems, vol. 21, no. 3, pp. 293–320, 2009. 
@article{JAAMAS09,
  author = {Amato, Christopher and Bernstein, Daniel S. and Zilberstein, Shlomo},
  title = {Optimizing fixed-size stochastic controllers for {POMDPs} and decentralized {POMDPs}},
  volume = {21},
  number = {3},
  pages = {293-320},
  journal = {Journal of Autonomous Agents and Multi-Agent Systems},
  year = {2009}
}
D. S. Bernstein, C. Amato, E. A. Hansen, and S. Zilberstein, “Policy Iteration for Decentralized Control of Markov Decision Processes,” Journal of Artificial Intelligence Research, vol. 34, pp. 89–132, 2009. 
@article{BernsteinJAIR09,
  author = {Bernstein, Daniel S. and Amato, Christopher and Hansen, Eric A. and Zilberstein, Shlomo},
  title = {Policy Iteration for Decentralized Control of {Markov} Decision Processes},
  year = {2009},
  volume = {34},
  journal = {Journal of Artificial Intelligence Research},
  pages = {89-132}
}

2008

C. Amato and S. Zilberstein, “What’s Worth Memorizing: Attribute-based Planning for DEC-POMDPs,” in Proceedings of the Multiagent Planning Workshop at ICAPS, Sydney, Australia, 2008. 
@inproceedings{ICAPSWorkshop08,
  author = {Amato, Christopher and Zilberstein, Shlomo},
  title = {What's Worth Memorizing: Attribute-based Planning for {DEC-POMDPs}},
  booktitle = {Proceedings of the Multiagent Planning Workshop at {ICAPS}},
  address = {Sydney, Australia},
  year = {2008}
}

2007

C. Amato, D. S. Bernstein, and S. Zilberstein, “Solving POMDPs Using Quadratically Constrained Linear Programs,” in Proceedings of the International Joint Conference on Artificial Intelligence, Hyderabad, India, 2007, pp. 2418–2424. 
@inproceedings{IJCAI07,
  author = {Amato, Christopher and Bernstein, Daniel S. and Zilberstein, Shlomo},
  title = {Solving {POMDPs} Using Quadratically Constrained Linear Programs},
  booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence},
  year = {2007},
  pages = {2418-2424},
  address = {Hyderabad, India}
}
C. Amato, D. S. Bernstein, and S. Zilberstein, “Optimizing Memory-Bounded Controllers for Decentralized POMDPs,” in Proceedings of the Conference on Uncertainty in Artificial Intelligence, Vancouver, Canada, 2007, pp. 1–8. 
@inproceedings{UAI07,
  author = {Amato, Christopher and Bernstein, Daniel S. and Zilberstein, Shlomo},
  title = {Optimizing Memory-Bounded Controllers for Decentralized {POMDPs}},
  booktitle = {Proceedings of the Conference on Uncertainty in Artificial Intelligence},
  year = {2007},
  pages = {1-8},
  address = {Vancouver, Canada}
}
C. Amato, A. Carlin, and S. Zilberstein, “Bounded Dynamic Programming for Decentralized POMDPs,” in Proceedings of the Workshop on Multi-Agent Sequential Decision Making in Uncertain Domains at AAMAS, Honolulu, Hawai’i, 2007. 
@inproceedings{MSDM07,
  author = {Amato, Christopher and Carlin, Alan and Zilberstein, Shlomo},
  title = {Bounded Dynamic Programming for Decentralized {POMDPs}},
  booktitle = {Proceedings of the Workshop on Multi-Agent Sequential Decision Making in Uncertain Domains at {AAMAS}},
  year = {2007},
  address = {Honolulu, Hawai'i}
}