Adrian K. Agogino
I am a researcher with the Robust Software Engineering Group
in the Intelligent Systems Division at NASA Ames Research Center,
employed through the University of California, Santa Cruz.
My research interests are in the fields of machine learning, complex
learning systems and multiagent control. My early work includes novel
visualization methods for complex systems and ways to better adapt
evolutionary algorithms to multiagent online environments. More recent
work includes using reinforcement learning in multiagent systems. Since
2004 I have been working on complex systems projects including air
traffic flow management, rocket engine analysis, and coordination in
multi-rover learning. My publications focus on the fields of
machine learning, multiagent systems, reinforcement learning,
evolutionary systems and visualization of complex systems.
- Agent-Based Resource Allocation in Dynamically Formed CubeSat
Constellations. C. HolmesParker, A. Agogino, In Proceedings of the
Tenth International Joint Conference
on Autonomous Agents and Multiagent Systems, 2011.
- A Multiagent Approach to Managing Air Traffic Flow. K. Tumer and
A. K. Agogino. Journal of Autonomous Agents and Multi-Agent Systems,
- Improving Air Traffic Management with A Learning Multiagent
System, K. Tumer and A. K. Agogino, Intelligent Systems, 24(1),
IEEE, Computer Society, 2009.
- Multiagent Learning for Black Box System Reward Functions, K.
Tumer and A. K. Agogino, Advances in Complex Systems, 2009.
- Analyzing and Visualizing Multiagent Rewards in Dynamic and
Stochastic Environments, A. K. Agogino and K. Tumer, Journal of
Autonomous Agents and Multi Agent Systems 2008.
- Distributed Agent-Based Air Traffic Flow Management, K. Tumer and
A. Agogino., In Proceedings of the Sixth International Joint Conference
on Autonomous Agents and Multiagent Systems, 2007. Best paper award
(Out of 531 submissions)
- Entropy Based Anomaly Detection Applied to Space Shuttle Main
Engines, A. Agogino and K. Tumer, In Proceedings of the IEEE Aerospace
- QUICR-Learning for Multiagent Coordination, A. Agogino and K.
Tumer, In Proceedings of the 21st National Conference on Artificial
- Distributed Evaluation Functions for Fault Tolerant Multi Rover
Systems, A. Agogino and K. Tumer, In Proceedings of the Genetic and
Evolutionary Computation Conference, July 2006.
- A Distributed and Adaptive Health and Mission Management
Architecture, K. Tumer, S. Uckun, and A. Agogino, In Integrated Systems
Health Management, 2005.
Intelligent Systems Division
Ames Research Center
Mail Stop 269-3
Moffett Field, CA 94035
Email:adrian.k.agogino at nasa.gov