Multiagent Traffic Management: Opportunities for Multiagent Learning

  • Kurt Dresner
  • Peter Stone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3898)


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.


Multiagent System Autonomous Agent Intersection Manager Autonomous Vehicle Reservation System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kurt Dresner
    • 1
  • Peter Stone
    • 1
  1. 1.Department of Computer SciencesUniversity of Texas at AustinAustinUSA

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