Papers
37
Total Citations
691
H-Index
12
About
Christopher Amato is a prominent researcher specializing in multi-agent decision-making, decentralized planning under uncertainty, and multi-robot coordination. His work centers on Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs), a mathematical framework for enabling teams of autonomous agents to cooperate effectively despite limited sensing, communication, and environmental uncertainty. Amato's most influential contribution, "Policy Iteration for Decentralized Control of Markov Decision Processes" (2009, 92 citations), established foundational algorithmic approaches for coordinating distributed agents across domains ranging from robotics to networking. He subsequently advanced the field by introducing macro-actions — higher-level, temporally extended behaviors — into Dec-POMDP frameworks, allowing robots to plan more efficiently in complex, continuous environments. This innovation appears across several highly cited works, including his 2015 and 2017 papers on belief space macro-actions (62 and 60 citations respectively). More recently, Amato has bridged classical planning with modern machine learning, developing reinforcement learning approaches for decentralized multi-robot coordination, as seen in his 2020 work on centralized Q-Net training for decentralized execution. His 2019 research on object search in cluttered environments further demonstrates his commitment to practical robotic applications. With hundreds of cumulative citations, Amato's research has meaningfully shaped how autonomous systems collaborate under real-world uncertainty.
Research Focus
Key Achievements
Top Papers
- 1Policy Iteration for Decentralized Control of Markov Decision Processes92 citations · 2009
- 2Planning for decentralized control of multiple robots under uncertainty88 citations · 2015
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- 5Modeling and Planning with Macro-Actions in Decentralized POMDPs62 citations · 2019
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- 7Policy search for multi-robot coordination under uncertainty48 citations · 2016
- 8Policy Search for Multi-Robot Coordination under Uncertainty36 citations · 2015
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