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Autonomous bidding agents : strategies and lessons from the trading agent competition / Michael P. Wellman, Amy Greenwald, and Peter Stone.

By: Wellman, Michael P [author.].

Contributor(s): Stone, Peter, 1971- | Greenwald, Amy | IEEE Xplore (Online Service) [distributor.] | MIT Press [publisher.].

Material type: materialTypeLabel

BookSeries: Intelligent robotics and autonomous agents: Publisher: Cambridge, Massachusetts : MIT Press, c2007Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [2007]Description: 1 PDF (xi, 238 pages) : illustrations.Content type: text Media type: electronic Carrier type: online resourceISBN: 9780262285957.Subject(s): Electronic commerce

| Intelligent agents (Computer software)

Genre/Form: Electronic books.Additional physical formats: Print version: No titleDDC classification: 338.4/3 Online resources: Abstract with links to resource Also available in print.

Contents:

Introduction -- The tac travel-shopping game -- Bidding in interdependent markets -- Price prediction -- Bidding with price predictions -- Machine learning and adaptivity -- Market-specific bidding strategies -- Experimental methods and strategic analysis -- Conclusion.

Summary: 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. Michael P. Wellman is Professor of Computer Science and Engineering and member of the Artificial Intelligence Laboratory at the University of Michigan, Ann Arbor. Amy Greenwald is Assistant Professor of Computer Science at Brown University. Peter Stone is Assistant Professor of Computer Sciences, Alfred P. Sloan Research Fellow, and Director of the Learning Agents Group at the University of Texas, Austin. He is the recipient of the International Joint Conference on Artificial Intelligence (IJCAI) 2007 Computers and Thought Award.

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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. Michael P. Wellman is Professor of Computer Science and Engineering and member of the Artificial Intelligence Laboratory at the University of Michigan, Ann Arbor. Amy Greenwald is Assistant Professor of Computer Science at Brown University. Peter Stone is Assistant Professor of Computer Sciences, Alfred P. Sloan Research Fellow, and Director of the Learning Agents Group at the University of Texas, Austin. He is the recipient of the International Joint Conference on Artificial Intelligence (IJCAI) 2007 Computers and Thought Award.

Central Library

Autonomous bidding agents : strategies and lessons from the trading agent competition / Michael P. Wellman, Amy Greenwald, and Peter Stone.

By: Wellman, Michael P [author.].

Contributor(s): Stone, Peter, 1971- | Greenwald, Amy | IEEE Xplore (Online Service) [distributor.] | MIT Press [publisher.].