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 @ cs.utexas.edu
Robert E. Schapire
Department of Computer Science, Princeton University
35 Olden Street, Princeton, NJ 08544 USA
schapire @ cs.princeton.edu
Michael L. Littman
Department of Computer Science, Rutgers University
Piscataway, NJ 08854-8019 USA
mlittman @ cs.rutgers.edu
János A. Csirik
D. E. Shaw & Co.
120 W 45th St, New York, NY 10036 USA
janos @ pobox.com
David McAllester
Toyota Technological Institute at Chicago
1427 East 60th Street, Chicago IL, 60637 USA
mcallester @ tti-chicago.edu

Abstract:

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.

Next: Introduction

Peter Stone 2003-09-10