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

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

Research output: Contribution to journalArticlepeer-review

52 Scopus citations


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 (TAG-01). We present experiments demonstrating the effectiveness of our boosting-based price predictor relative to several reasonable alternatives.

Original languageEnglish (US)
Pages (from-to)209-242
Number of pages34
JournalJournal of Artificial Intelligence Research
StatePublished - 2003

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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