Kurt Dresner's Publications

Sorted by DateClassified by Publication TypeClassified by Research Category

Multiagent Traffic Management: Opportunities for Multiagent Learning

Kurt Dresner and Peter Stone. Multiagent Traffic Management: Opportunities for Multiagent Learning. In K. Tuyls et al., editors, LAMAS 2005, Lecture Notes In Artificial Intelligence, pp. 129–138, Springer Verlag, Berlin, 2006.

Download

[PDF]81.4kB  [gzipped postscript]50.0kB  

Abstract

Traffic Congestion is one of the leading causes of lost productivityand decreased standard of living in urban settings. In previous workpublished at AAMAS, we have proposed a novel reservation-basedmechanism for increasing throughput and decreasing delays atintersections. In more recent work, we have provided a detailedprotocol by which two different classes of agents (intersectionmanagers and driver agents) can use this system. We believe that thedomain created by this mechanism and protocol presents manyopportunities for multiagent learning on the parts of both classes ofagents. In this paper, we identify several of these opportunities andoffer a first-cut approach to each.

BibTeX Entry

@incollection{2005lamas-dresner,
  author="Kurt Dresner and Peter Stone",
  title="Multiagent Traffic Management: Opportunities for Multiagent Learning",
  booktitle="LAMAS 2005",
  series="Lecture Notes In Artificial Intelligence",
  editor="{K.~Tuyls et al.}",
  year="2006",
  pages="129--138",
  publisher="Springer Verlag",
  address="Berlin",
  volume="3898",
  abstract={ 
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.  
  },
  bib2html_rescat = {Autonomous Intersection Management},
  bib2html_pubtype = {Workshop}
}

Generated by bib2html (written by Patrick Riley ) on Wed Jul 02, 2008 11:31:21