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