Peter Stone's Selected Publications

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Autonomous Bidding Agents: Strategies and Lessons from the Trading Agent Competition

Michael P. Wellman, Amy Greenwald, and Peter Stone. Autonomous Bidding Agents: Strategies and Lessons from the Trading Agent Competition, MIT Press, 2007.
Available from MIT Press page.
ISBN: 0-262-23260-X

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Abstract

E-commerce increasingly provides opportunities for autonomous bidding agents: computer programs that bid in electronic markets without direct human intervention. Automated bidding strategies for an auction of a single good with a known valuation are fairly straightforward; designing strategies for simultaneous auctions with interdependent valuations is a more complex undertaking. This book presents algorithmic advances and strategy ideas within an integrated bidding agent architecture that have emerged from recent work in this fast-growing area of research in academia and industry. The authors analyze several novel bidding approaches that developed from the Trading Agent Competition (TAC), held annually since 2000. The benchmark challenge for competing agents--to buy and sell multiple goods with interdependent valuations in simultaneous auctions of different types--encourages competitors to apply innovative techniques to a common task. The book traces the evolution of TAC and follows selected agents from conception through several competitions, presenting and analyzing detailed algorithms developed for autonomous bidding. "Autonomous Bidding Agents" provides the first integrated treatment of methods in this rapidly developing domain of AI. The authors--who introduced TAC and created some of its most successful agents--offer both an overview of current research and new results.

BibTeX Entry

@book(Stone:tacbook,
        Author="Michael P.~Wellman and Amy Greenwald and Peter Stone",
        title="Autonomous Bidding Agents:  Strategies and Lessons from the Trading Agent Competition",
        publisher="MIT Press",
        year="2007",
        abstract={
                  E-commerce increasingly provides opportunities for
                  autonomous bidding agents: computer programs that
                  bid in electronic markets without direct human
                  intervention. Automated bidding strategies for an
                  auction of a single good with a known valuation are
                  fairly straightforward; designing strategies for
                  simultaneous auctions with interdependent valuations
                  is a more complex undertaking. This book presents
                  algorithmic advances and strategy ideas within an
                  integrated bidding agent architecture that have
                  emerged from recent work in this fast-growing area
                  of research in academia and industry.
                  The authors analyze several novel bidding approaches
                  that developed from the Trading Agent Competition
                  (TAC), held annually since 2000. The benchmark
                  challenge for competing agents--to buy and sell
                  multiple goods with interdependent valuations in
                  simultaneous auctions of different types--encourages
                  competitors to apply innovative techniques to a
                  common task. The book traces the evolution of TAC
                  and follows selected agents from conception through
                  several competitions, presenting and analyzing
                  detailed algorithms developed for autonomous
                  bidding.
                  "Autonomous Bidding Agents" provides the first
                  integrated treatment of methods in this rapidly
                  developing domain of AI. The authors--who introduced
                  TAC and created some of its most successful
                  agents--offer both an overview of current research
                  and new results.
        },
        wwwnote={Available from <a href="http://mitpress.mit.edu/026223260x">MIT Press</a> page.<br>
                 ISBN: 0-262-23260-X
        },
)

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