Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The Air Traffic Flow Management Problem with Enroute Capacities
Operations Research
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
Handling Communication Restrictions and Team Formation in Congestion Games
Autonomous Agents and Multi-Agent 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
Regulating air traffic flow with coupled agents
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Decentralized algorithms for collision avoidance in airspace
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Agent-Based Approach to Free-Flight Planning, Control, and Simulation
IEEE Intelligent Systems
Improving Air Traffic Management with a Learning Multiagent System
IEEE Intelligent Systems
IEEE Transactions on Intelligent Transportation Systems
Dynamic network flow model for short-term air traffic flow management
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Agent and multi-agent applications to support distributed communities of practice: a short review
Autonomous Agents and Multi-Agent Systems
Shaping fitness functions for coevolving cooperative multiagent systems
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Towards a satisfactory conversion of messages among agent-based information systems
Expert Systems with Applications: An International Journal
Predicting behavior in unstructured bargaining with a probability distribution
Journal of Artificial Intelligence Research
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Intelligent air traffic flow management is one of the fundamental challenges facing the Federal Aviation Administration (FAA) today. FAA estimates put weather, routing decisions and airport condition induced delays at 1,682,700 h in 2007 (FAA OPSNET Data, US Department of Transportation website, http://www.faa.gov/data_statistics/ ), resulting in a staggering economic loss of over $41 billion (Joint Economic Commission Majority Staff, Your flight has been delayed again, 2008). New solutions to the flow management are needed to accommodate the threefold increase in air traffic anticipated over the next two decades. Indeed, this is a complex problem where the interactions of changing conditions (e.g., weather), conflicting priorities (e.g., different airlines), limited resources (e.g., air traffic controllers) and heavy volume (e.g., over 40,000 flights over the US airspace) demand an adaptive and robust solution. In this paper we explore a multiagent algorithm where agents use reinforcement learning (RL) to reduce congestion through local actions. Each agent is associated with a fix (a specific location in 2D space) and has one of three actions: setting separation between airplanes, ordering ground delays or performing reroutes. We simulate air traffic using FACET which is an air traffic flow simulator developed at NASA and used extensively by the FAA and industry. Our FACET simulations on both artificial and real historical data from the Chicago and New York airspaces show that agents receiving personalized rewards reduce congestion by up to 80% over agents receiving a global reward and by up to 90% over a current industry approach (Monte Carlo estimation).