Journal of Artificial Intelligence Research, 19 (2003) 209-242. Submitted 12/02; published 9/03

© 2003 AI Access Foundation and Morgan Kaufmann Publishers. All rights reserved.

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Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions

Peter Stone
Department of Computer Sciences, The University of Texas at Austin
1 University Station C0500, Austin, TX 78712 USA
pstone @

Robert E. Schapire
Department of Computer Science, Princeton University
35 Olden Street, Princeton, NJ 08544 USA
schapire @

Michael L. Littman
Department of Computer Science, Rutgers University
Piscataway, NJ 08854-8019 USA
mlittman @

János A. Csirik
D. E. Shaw & Co.
120 W 45th St, New York, NY 10036 USA
janos @

David McAllester
Toyota Technological Institute at Chicago
1427 East 60th Street, Chicago IL, 60637 USA
mcallester @


Auctions are becoming an increasingly popular method for transacting business, especially over the Internet. This article presents a general approach to building autonomous bidding agents to bid in multiple simultaneous auctions for interacting goods. A core component of our approach learns a model of the empirical price dynamics based on past data and uses the model to analytically calculate, to the greatest extent possible, optimal bids. We introduce a new and general boosting-based algorithm for conditional density estimation problems of this kind, i.e., supervised learning problems in which the goal is to estimate the entire conditional distribution of the real-valued label. This approach is fully implemented as ATTac-2001, a top-scoring agent in the second Trading Agent Competition (TAC-01). We present experiments demonstrating the effectiveness of our boosting-based price predictor relative to several reasonable alternatives.

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Peter Stone 2003-09-10