Contact The MIT Press Information on how to order from The MIT Press Access your saved shopping cart, e-mail list subscriptions, order history, address book, and other info in the Your Profile area MIT Press Home Page


August 2007
6 x 9, 256 pp., 25 illus.
$35.00/£25.95 (CLOTH)
Short

ISBN-10:
0-262-23260-X
ISBN-13:
978-0-262-23260-9

Series
Intelligent Robotics and Autonomous Agents
Related Links
Open this site in a new browser window.
Find this book in a library
Preview or Purchase the E-Book Version of This Title
Autonomous Bidding Agents
Strategies and Lessons from the Trading Agent Competition
Michael P. Wellman, Amy Greenwald and Peter Stone

Table of Contents and Sample Chapters

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.

About the Authors

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, University of Texas at Austin. He is the recipient of the International Joint Conference on Artificial Intelligence (IJCAI) 2007 Computers and Thought Award.




See Other Titles In:
Computer Science and Intelligent Systems
 Adaptive Computation & Machine Learning
 Artificial Intelligence
 Complex Adaptive Systems
 Intelligent Systems
Economics, Finance, and Business
 Information Technology
Science, Technology, and Society
 Engineering
 
Join an E-mail Alert List


 
 
TECHNOLOGY PARTNER: Azility, Inc. TERMS OF USE | PRIVACY POLICY | COPYRIGHT © 2009