Computer Science – Artificial Intelligence
Scientific paper
2011-06-26
Journal Of Artificial Intelligence Research, Volume 19, pages 209-242, 2003
Computer Science
Artificial Intelligence
Scientific paper
10.1613/jair.1200
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.
Csirik János A.
Littman Michael L.
McAllester David
Schapire Robert E.
Stone Peter
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