UTCS Artificial Intelligence
Autonomous Bidding Agents: Strategies and Lessons from the Trading Agent Competition (2007)
Michael P. Wellman, Amy Greenwald, and
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.
pstone [at] cs utexas edu
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