Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Symbiotic evolution of neural networks in sequential decision tasks
Symbiotic evolution of neural networks in sequential decision tasks
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Modeling and simulation of the automated highway system
Modeling and simulation of the automated highway system
The BATmobile: towards a Bayesian automated taxi
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Mining GPS data to augment road models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Distributed reinforcement learning for a traffic engineering application
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Multiagent traffic management: an improved intersection control mechanism
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Anticipatory Behavior in Adaptive Learning Systems
Traffic intersections of the future
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
A multiagent approach to autonomous intersection management
Journal of Artificial Intelligence Research
Sharing the road: autonomous vehicles meet human drivers
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multiagent traffic management: opportunities for multiagent learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
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This paper presents a novel approach to traffic management by coordinating driver behaviors. Current traffic management systems do not consider lane organization of the cars and only affect traffic flows by controlling traffic signals or ramp meters. However, drivers should be able to increase traffic throughput and more consistently maintain desired speeds by selecting lanes intelligently. We pose the problem of intelligent lane selection as a challenging and potentially rewarding problem for artificial intelligence, and we propose a methodology that uses supervised and reinforcement learning to form distributed control strategies. Initial results are promising and demonstrate that intelligent lane selection can better approximate desired speeds and reduce the total number of lane changes.