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

Cover Image Copyright Year: 2007
Author(s): Wellman, M.; Greenwald, A.; Stone, P.
Publisher: MIT Press
Content Type : Books & eBooks
Topics: Computing & Processing (Hardware/Software)
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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. 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, A ustin. He is the recipient of the International Joint Conference on Artificial Intelligence (IJCAI) 2007 Computers and Thought Award.

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      Front Matter

      Wellman, M. ; Greenwald, A. ; Stone, P.
      Autonomous Bidding Agents:Strategies and Lessons from the Trading Agent Competition

      Page(s): i - xi
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains sections titled: Half Title, Intelligent Robotics and Autonomous Agents, Title, Copyright, Dedication, Contents, Preface, Acknowledgments View full abstract»

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      Introduction

      Wellman, M. ; Greenwald, A. ; Stone, P.
      Autonomous Bidding Agents:Strategies and Lessons from the Trading Agent Competition

      Page(s): 1 - 8
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains sections titled: Trading Agent Research, Trading Agents Competing, Book Overview View full abstract»

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      The TAC Travel-Shopping Game

      Wellman, M. ; Greenwald, A. ; Stone, P.
      Autonomous Bidding Agents:Strategies and Lessons from the Trading Agent Competition

      Page(s): 9 - 32
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains sections titled: TAC Market Game, Game Operations, Competition History View full abstract»

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      Bidding in Interdependent Markets

      Wellman, M. ; Greenwald, A. ; Stone, P.
      Autonomous Bidding Agents:Strategies and Lessons from the Trading Agent Competition

      Page(s): 33 - 59
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains sections titled: A Generic Bidding Cycle, Bid Determination Problems, Marginal Values and Prices, The Bidding Cycle, Revisited View full abstract»

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      Price Prediction

      Wellman, M. ; Greenwald, A. ; Stone, P.
      Autonomous Bidding Agents:Strategies and Lessons from the Trading Agent Competition

      Page(s): 61 - 79
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains sections titled: Predicting TAC Hotel Prices, TAC-02 Agents, Price-Prediction Survey, Approaches to Price Prediction, Predictions, Evaluating Prediction Quality, Discussion View full abstract»

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      Bidding with Price Predictions

      Wellman, M. ; Greenwald, A. ; Stone, P.
      Autonomous Bidding Agents:Strategies and Lessons from the Trading Agent Competition

      Page(s): 81 - 116
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains sections titled: Auction Framework, Optimal Bidding with Known Prices, Bidding with Point Price Predictions, Bidding with Distributional Price Predictions, Experiments in TAC Travel Auctions, Discussion View full abstract»

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      Machine Learning and Adaptivity

      Wellman, M. ; Greenwald, A. ; Stone, P.
      Autonomous Bidding Agents:Strategies and Lessons from the Trading Agent Competition

      Page(s): 117 - 142
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains sections titled: Adaptivity for Last-Moment Bidding, Learning Distributions for Hotel Price Prediction, ATTac-01, ATTac-01 Results, Summary View full abstract»

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      Market-Specific Bidding Strategies

      Wellman, M. ; Greenwald, A. ; Stone, P.
      Autonomous Bidding Agents:Strategies and Lessons from the Trading Agent Competition

      Page(s): 143 - 167
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains sections titled: Flight Buying, Hotel-Bidding Strategies, Trading Entertainment Tickets, Discussion View full abstract»

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      Experimental Methods and Strategic Analysis

      Wellman, M. ; Greenwald, A. ; Stone, P.
      Autonomous Bidding Agents:Strategies and Lessons from the Trading Agent Competition

      Page(s): 169 - 194
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains sections titled: Strategic Interactions, Hierarchical Game Reduction, Control Variates, Walverine Parameters, TAC Experiments, Discussion View full abstract»

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      Conclusion

      Wellman, M. ; Greenwald, A. ; Stone, P.
      Autonomous Bidding Agents:Strategies and Lessons from the Trading Agent Competition

      Page(s): 195 - 203
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains sections titled: Multiagent Competitions and Research, Concluding Remarks View full abstract»

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      Tournament Data

      Wellman, M. ; Greenwald, A. ; Stone, P.
      Autonomous Bidding Agents:Strategies and Lessons from the Trading Agent Competition

      Page(s): 205 - 218
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains sections titled: 2000, 2001, 2002, 2003, 2004, 2005, 2006 View full abstract»

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      Integer Linear Programming Formulations

      Wellman, M. ; Greenwald, A. ; Stone, P.
      Autonomous Bidding Agents:Strategies and Lessons from the Trading Agent Competition

      Page(s): 219 - 225
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains sections titled: The Sample Average Approximation of the TAC Travel Bidding Problem, TAC Travel Completion Problem, TAC Travel Acquisition Problem, TAC Travel Allocation Problem View full abstract»

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      References

      Wellman, M. ; Greenwald, A. ; Stone, P.
      Autonomous Bidding Agents:Strategies and Lessons from the Trading Agent Competition

      Page(s): 227 - 232
      Copyright Year: 2007

      MIT Press eBook 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. 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, A ustin. He is the recipient of the International Joint Conference on Artificial Intelligence (IJCAI) 2007 Computers and Thought Award. View full abstract»

    • Full text access may be available. Click article title to sign in or learn about subscription options.

      Citation Index

      Wellman, M. ; Greenwald, A. ; Stone, P.
      Autonomous Bidding Agents:Strategies and Lessons from the Trading Agent Competition

      Page(s): 233 - 234
      Copyright Year: 2007

      MIT Press eBook 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. 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, A ustin. He is the recipient of the International Joint Conference on Artificial Intelligence (IJCAI) 2007 Computers and Thought Award. View full abstract»

    • Full text access may be available. Click article title to sign in or learn about subscription options.

      Subject Index

      Wellman, M. ; Greenwald, A. ; Stone, P.
      Autonomous Bidding Agents:Strategies and Lessons from the Trading Agent Competition

      Page(s): 235 - 238
      Copyright Year: 2007

      MIT Press eBook 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. 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, A ustin. He is the recipient of the International Joint Conference on Artificial Intelligence (IJCAI) 2007 Computers and Thought Award. View full abstract»