Christopher AmatoAssistant Professor
Department of Computer Science
University of New Hampshire
camato at cs dot unh dot edu
I'm looking for talented PhD students interested in AI, machine learning and robotics. If this is you, apply and email me!
Previously, I was Research Scientist (and a Postdoc) at MIT working with Leslie Kaelbling and the Learning and Intelligent Systems group in CSAIL as well as Jon How and the Aerospace Control Lab in LIDS. I have a PhD in Computer Science from UMass Amherst, where I was advised by Shlomo Zilberstein. My research interests include Artificial Intelligence, Robotics, Multi-Agent and Multi-Robot Systems, Reasoning Under Uncertainty, Game Theory and Machine Learning.
My research explores principled solution methods for systems of agents (e.g., robots, network nodes, sensors, people) with uncertainty and limited communication. As agents are built for more complex environments, engineering solutions by hand becomes very difficult, while methods based on formal models can automatically generate high-quality solutions. Many real-world scenarios have some communication cost, latency or noise (e.g., disaster response, networking, coordination over large distances). Due to these communication limitations, agents that can make decisions on their own are critical. My research seeks to develop fundamental theory as well as scalable algorithms that provide this high-quality autonomy in real-world systems such as multi-robot navigation, search and rescue and surveillance problems.
Press:Much of my work focuses on robotics, and I'm very interested in using my work for real-world applications. Here are some press articles about my work.
Optimizing communication and behavior for teams of robots:
Recent and upcoming events:We have a new website for UNH Robotics.
Our RSS paper, Policy Search for Multi-Robot Coordination under Uncertainty, was nominated for the best paper award! It can be downloaded here.
We are organizing a AAAI Fall Symposium on sequential decision-making under uncertainty to bring together researchers working on MDP-based models for single and multi-agent systems. Check out the link for more info.
Our AAMAS paper, Exploiting Separability in Multi-Agent Planning with Continuous-State MDPs, won best paper! It can be downloaded here.
We have a combined tutorial and workshop at AAMAS-14 on Multiagent Sequential Decision Making Under Uncertainty. Check out the website here.
We had a pair of tutorials, one on Self-Interested Decision Making in Sequential Multiagent Settings and one on Cooperative Decision Making in Sequential Multiagent Settings at AAMAS-13 with Prashant Doshi, Frans Oliehoek, Zinovi Rabinovich, Matthijs Spaan and Stefan Witwicki. You can see the website here.
I co-organized the workshop on Decision Making in Partially Observable, Uncertain Worlds: Exploring Insights from Multiple Communities at IJCAI-11. For more info, check out the website.
Other links:I maintain the Dec-POMDP page which contains information about the decentralized partially observable Markov decision process (Dec-POMDP) model for describing multiagent decision making under uncertainty. Check it out for an overview, publications, talks and code for various datasets.
While working at Microsoft Research over the summer, I developed a reinforcement learning framework for the video game Civilization IV. You can download it and be able to have the AI learn to improve its play with different RL algorithms. Check it out at the MSR website.