Collective Intelligence and Braess' Paradox
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Selection of information types based on personal utility: a testbed for traffic information markets
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Opportunities for multiagent systems and multiagent reinforcement learning in traffic control
Autonomous Agents and Multi-Agent Systems
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
Empowered by wireless communication: Distributed methods for self-organizing traffic collectives
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
Avoid fixed pricing: consume less, earn more, make clients happy
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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In multi-agent systems, greedly agents can harm the performance of the overall system. This is the case of traffic commuting scenarios: drivers repete their actions trying to adapt to daily changes. In this domain, there are several proposals to achieve the traffic network equilibrium. Recently, the focus has shifted to information provision in several forms as a way to balance the load. Most of these works make strong assumptions such as the traffic authority and/or drivers having perfect information. In reality, the information the central control provides to drivers contains estimation errors. The goal of this paper is to propose a socially efficient load balance by internalizing social costs caused by agents' actions. Two issues are addressed: the model of information provision accounts for information imperfectness, and the equilibrium which emerges out of drivers route choices is close to the system optimum due to mechanisms of road pricing. The model can then be used for traffic authorities to simulate the effects of information provision and toll charging.