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. 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. 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.

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In LAMAS 2005, K. Tuyls et al. (Eds.), Vol. 3898, pp. 129-138, Berlin 2006. Springer Verlag.

@incollection{LAMAS05-kurt, title={Multiagent Traffic Management: Opportunities for Multiagent Learning}, author={Kurt Dresner and Peter Stone}, booktitle={LAMAS 2005}, volume={3898}, editor={K. Tuyls et al.}, series={Lecture Notes in Artificial Intelligence}, address={Berlin}, publisher={Springer Verlag}, pages={129-138}, url="http://www.cs.utexas.edu/users/ai-lab?LAMAS05-kurt", year={2006} }