In complicated, interacting auctions, a fundamental problem is the prediction of prices of goods in the auctions, and more broadly, the modeling of uncertainty regarding these prices. In this paper, we present a machine-learning approach to this problem. The technique is based on 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 algorithm, which we present in detail, is at the heart of ATTac-2001, a top-scoring agent in the recent Trading Agent Competition (TAC-01). We describe how ATTac-2001 works, the results of the competition, and controlled experiments evaluating the effectiveness of price prediction in auctions.

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In Proceedings of the Nineteenth International Conference on Machine Learning, 2002.

@inproceedings{ICML02-tac, title={Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation}, author={Robert E. Schapire and Peter Stone and David McAllester and Michael L. Littman and J'anos A. Csirik}, booktitle={Proceedings of the Nineteenth International Conference on Machine Learning}, url="http://nn.cs.utexas.edu/?ICML02-tac", year={2002} }