Decision-theoretic bidding based on learned density models in simultaneous, interacting auctions

  • Authors:

  • Peter Stone;Robert E. Schapire;Michael L. Littman;János A. Csirik;David McAllester

  • Affiliations:

  • Dept. of Computer Sciences, The University of Texas at Austin, Austin, Texas;Department of Computer Science, Princeton University, Princeton, NJ;Dept. of Computer Science, Rutgers University, Piscataway, NJ;D. E. Shaw & Co., New York, NY;Toyota Technological Institute at Chicago, Chicago, IL

  • Venue:
  • Journal of Artificial Intelligence Research
  • Year:
  • 2003

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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.