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
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
Evolving distributed agents for managing air traffic
Proceedings of the 9th annual conference on Genetic and evolutionary computation
To BDI, or not to BDI: design choices in an agent-based traffic flow management simulation
Proceedings of the 2008 Spring simulation multiconference
Regulating air traffic flow with coupled agents
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Market-based coordination for intersection control
Proceedings of the 2009 ACM symposium on Applied Computing
A market-inspired approach to reservation-based urban road traffic management
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Improving air traffic management through agent suggestions
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Learning from actions not taken: a multiagent learning algorithm
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Evaluating evolution and monte carlo for controlling air traffic flow
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Agent-based distributed decision-making in dynamic operational environments
Intelligent Decision Technologies
Adaptive management of air traffic flow: a multiagent coordination approach
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Recovering from Airline Operational Problems with a Multi-Agent System: A Case Study
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Future challenges for autonomous systems
Artificial intelligence
A review of the applications of agent technology in traffic and transportation systems
IEEE Transactions on Intelligent Transportation Systems
A neuro-evolutionary approach to micro aerial vehicle control
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Component evolution for large scale air traffic optimization
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Can we predict safety culture?
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: Industry track
An agent-based approach for structured modeling, analysis and improvement of safety culture
Computational & Mathematical Organization Theory
Real-time trip information service for a large taxi fleet
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
A multiagent approach to managing air traffic flow
Autonomous Agents and Multi-Agent Systems
Coordinating learning agents for multiple resource job scheduling
ALA'09 Proceedings of the Second international conference on Adaptive and Learning Agents
Modeling difference rewards for multiagent learning
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
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Air traffic flow management is one of the fundamental challenges facing the Federal Aviation Administration (FAA) today. The FAA estimates that in 2005 alone, there were over 322,000 hours of delays at a cost to the industry in excess of three billion dollars. Finding reliable and adaptive solutions to the flow management problem is of paramount importance if the Next Generation Air Transportation Systems are to achieve the stated goal of accommodating three times the current traffic volume. This problem is particularly complex as it requires the integration and/or coordination of many factors including: new data (e.g., changing weather info), potentially conflicting priorities (e.g., different airlines), limited resources (e.g., air traffic controllers) and very heavy traffic volume (e.g., over 40,000 flights over the US airspace). In this paper we use FACET -- an air traffic flow simulator developed at NASA and used extensively by the FAA and industry -- to test a multi-agent algorithm for traffic flow management. An agent is associated with a fix (a specific location in 2D space) and its action consists of setting the separation required among the airplanes going though that fix. Agents use reinforcement learning to set this separation and their actions speed up or slow down traffic to manage congestion. Our FACET based results show that agents receiving personalized rewards reduce congestion by up to 45% over agents receiving a global reward and by up to 67% over a current industry approach (Monte Carlo estimation).