Peter Stone's Selected Publications

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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.
LAMAS-05.
Official version from Publisher's Webpage© Springer-Verlag

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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.

BibTeX Entry

@incollection(LAMAS05-kurt,
        author="Kurt Dresner and Peter Stone",
        title="Multiagent Traffic Management: Opportunities for Multiagent Learning",
        booktitle="LAMAS 2005",
        editor="K.~Tuyls et al.",
        series="Lecture Notes in Artificial Intelligence",      
	volume="3898",
	pages="129--138",
        Publisher="Springer Verlag",address="Berlin",year="2006",
        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.
                  },
        wwwnote={<a href="http://lamas2005.luc.ac.be/">LAMAS-05</a>.<br> 
Official version from <a href="http://dx.doi.org/10.1007/11691839_7">Publisher's Webpage</a>&copy Springer-Verlag
},
)

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