Technical Note: \cal Q-Learning
Machine Learning
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
Multiagent traffic management: an improved intersection control mechanism
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Impact of problem centralization in distributed constraint optimization algorithms
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Vehicular Mobility Simulation for VANETs
ANSS '07 Proceedings of the 40th Annual Simulation Symposium
Toward an understanding of flow in video games
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
Evaluating the performance of DCOP algorithms in a real world, dynamic problem
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Opportunities for multiagent systems and multiagent reinforcement learning in traffic control
Autonomous Agents and Multi-Agent Systems
Augmented experiment: participatory design with multiagent simulation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Decentralized coordination of plug-in hybrid vehicles for imbalance reduction in a smart grid
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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Multi-agent systems have already been successfully applied to a variety of traffic control problems and demonstrated the potential to lower travel times and environmental impact. Sharing this goal, we have developed iCO2, an online tool for training eco-friendly driving in a multi-user three-dimensional environment. iCO2 supports eco-driving practice by instructing computer-controlled agents, such as traffic lights and other vehicles, to create traffic situations that make eco-driving more difficult. Hence the agents take the role of "opponents" that try to achieve the optimal challenge level for the skill level of each user. The research challenge is to find the optimal challenge level for all user drivers in a shared simulation space that (1) involves both controllable entities ("opponents") and non-controllable entities (users) and (2) is highly dynamic, with dependencies between entities being created and destroyed in real time. We try to solve this problem by modeling the scenario as a distributed constraint optimization problem (DCOP). The main contribution of our paper is the application of a DCOP algorithm to such a new type of application scenario. We evaluate our approach by running scenarios both in terms of speed and optimality of the solutions proposed by the DCOP algorithm.