Learning cooperative lane selection strategies for highways
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An adaptive interactive agent for route advice
Proceedings of the third annual conference on Autonomous Agents
Neural Network Perception for Mobile Robot Guidance
Neural Network Perception for Mobile Robot Guidance
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Cooperative Autonomous Driving at the Intelligent Control Systems Laboratory
IEEE Intelligent Systems
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
A Distributed Approach for Coordination of Traffic Signal Agents
Autonomous Agents and Multi-Agent Systems
Multiagent traffic management: an improved intersection control mechanism
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
A Collaborative Reinforcement Learning Approach to Urban Traffic Control Optimization
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Opportunities for multiagent systems and multiagent reinforcement learning in traffic control
Autonomous Agents and Multi-Agent 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
Real-time trip information service for a large taxi fleet
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
An overview of cooperative and competitive multiagent learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
| Hi-index | 0.00 |
Traffic congestion is one of the leading causes of lost productivity and decreased standard of living in urban settings. In previous work published at AAMAS, we have proposed a novel reservation-based mechanism for increasing throughput and decreasing delays at intersections [3]. In more recent work, we have provided a detailed protocol by which two different classes of agents (intersection managers and driver agents) can use this system [4]. We believe that the domain created by this mechanism and protocol presents many opportunities for multiagent learning on the parts of both classes of agents. In this paper, we identify several of these opportunities and offer a first-cut approach to each.