Peter Stone's Selected Publications

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FAucS: An FCC Spectrum Auction Simulator for Autonomous Bidding Agents

János A. Csirik, Michael L. Littman, Satinder Singh, and Peter Stone. FAucS: An FCC Spectrum Auction Simulator for Autonomous Bidding Agents. In Electronic Commerce: Proceedings of the Second International Workshop, pp. 139–151, Springer Verlag, Heidelberg, Germany, 2001.
WELCOM-01

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Abstract

We introduce FAucS, a software testbed for studying automated agent bidding strategies in simulated auctions, specifically the United States FCC wireless frequency spectrum auctions. In addition to the complexity of these auctions, which provides ample opportunities for intelligent approaches to bidding, this type of auction has huge commercial importance, each bringing in billions of dollars to governments around the world. We implement straightforward sample agents in FAucS and use them to replicate known beneficial bidding strategies in this type of auction. We then discuss potential in-depth studies of autonomous bidding agent behaviors using FAucS. The main contribution of this work is the implementation, description, and empirical validation of the FAucS testbed. We present it as a challenging and promising AI research domain.

BibTeX Entry

@Inproceedings(FCC01,
        title="{FAucS}: An {FCC} Spectrum Auction Simulator for Autonomous Bidding Agents",
        author="J\'{a}nos A. Csirik and Michael L. Littman and Satinder Singh and Peter Stone",
        booktitle="Electronic Commerce:  Proceedings of the Second International Workshop",year="2001",
        editor={Ludger Fiege and Gero M\"{u}hl and Uwe Wilhelm},
        publisher="Springer Verlag",address="Heidelberg, Germany",
        pages="139--151",
        abstract={
                  We introduce FAucS, a software testbed for studying
                  automated agent bidding strategies in simulated
                  auctions, specifically the United States FCC
                  wireless frequency spectrum auctions.  In addition
                  to the complexity of these auctions, which provides
                  ample opportunities for intelligent approaches to
                  bidding, this type of auction has huge commercial
                  importance, each bringing in billions of dollars to
                  governments around the world.  We implement
                  straightforward sample agents in FAucS and use them
                  to replicate known beneficial bidding strategies in
                  this type of auction.  We then discuss potential
                  in-depth studies of autonomous bidding agent
                  behaviors using FAucS.  The main contribution of
                  this work is the implementation, description, and
                  empirical validation of the FAucS testbed.  We
                  present it as a challenging and promising AI
                  research domain.
        },
        wwwnote={<a href="http://www.informatik.tu-darmstadt.de/GK/welcom/">WELCOM-01</a>},
)

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