Technical Note: \cal Q-Learning
Machine Learning
Multi-Agent Reinforcement Leraning for Traffic Light Control
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Multiagent Traffic Management: A Reservation-Based Intersection Control Mechanism
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Learning from Multiple Sources
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
A Distributed Approach for Coordination of Traffic Signal Agents
Autonomous Agents and Multi-Agent Systems
Using cooperative mediation to coordinate traffic lights: a case study
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Dealing with non-stationary environments using context detection
ICML '06 Proceedings of the 23rd international conference on Machine learning
ITSUMO: an Intelligent Transportation System for Urban Mobility
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Traffic lights control with adaptive group formation based on swarm intelligence
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Re-routing Agents in an Abstract Traffic Scenario
SBIA '08 Proceedings of the 19th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Opportunities for multiagent systems and multiagent reinforcement learning in traffic control
Autonomous Agents and Multi-Agent Systems
A simulation-based investigation of a dynamic advanced traveler information system
Winter Simulation Conference
Learning dynamic adaptation strategies in agent-based traffic simulation experiments
MATES'11 Proceedings of the 9th German conference on Multiagent system technologies
Agent-Based Route (and Mode) Choice Simulation in Real-World Networks
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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One way to cope with the increasing traffic demand is to integrate standard solutions with more intelligent control measures. However, the result of possible interferences between intelligent control or information provision tools and other components of the overall traffic system is not easily predictable. This paper discusses the effects of integrating co-adaptive decision-making regarding route choices (by drivers) and control measures (by traffic lights). The motivation behind this is that optimization of traffic light control is starting to be integrated with navigation support for drivers. We use microscopic, agent-based modelling and simulation, in opposition to the classical network analysis, as this work focuses on the effect of local adaptation. In a scenario that exhibits features comparable to real-world networks, we evaluate different types of adaptation by drivers and by traffic lights, based on local perceptions. In order to compare the performance, we have also used a global level optimization method based on genetic algorithms.