Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Collectives and Design Complex Systems
Collectives and Design Complex Systems
Multi-agent reward analysis for learning in noisy domains
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
A cooperative multi-agent approach to free flight
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Autonomous agents for air-traffic deconfliction
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Achieving cooperation among selfish agents in the air traffic management domain using signed money
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Distributed agent-based air traffic flow management
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Adaptive management of air traffic flow: a multiagent coordination approach
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Static and expanding grid coverage with ant robots: Complexity results
Theoretical Computer Science
Multi-agent Cooperative Cleaning of Expanding Domains
International Journal of Robotics Research
A multiagent approach to managing air traffic flow
Autonomous Agents and Multi-Agent Systems
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The ability to provide flexible, automated management of air traffic is critical to meeting the ever increasing needs of the next generation air transportation systems. This problem is particularly complex as it requires the integration of many factors including, updated information (e.g., changing weather info), conflicting priorities (e.g., different airlines), limited resources (e.g., air traffic controllers) and very heavy traffic volume (e.g., over 40,000 daily flights over the US airspace). Furthermore, because the Federal Flight Administration will not accept black-box solutions, algorithmic improvements need to be consistent with current operating practices and provide explanations for each new decision. Unfortunately current methods provide neither flexibility for future upgrades, nor high enough performance in complex coupled air traffic flow problems. This paper extends agent-based methods for controlling air traffic flow to more realistic domains that have coupled flow patterns and need to be controlled through a variety of mechanisms. First, we explore an agent control structure that allows agents to control air traffic flow through one of three mechanisms (miles in trail, ground delays and rerouting). Second, we explore a new agent learning algorithm that can efficiently handle coupled flow patterns. We then test this agent solution on a series of congestion problems, showing that it is flexible enough to achieve high performance with different control mechanisms. In addition the results show that the new solution is able to achieve up to a 20% increase in performance over previous methods that did not account for the agent coupling.