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Auctions are becoming an increasingly popular method for transacting business, especially over the Internet. This paper presents a general approach to building autonomous bidding agents to bid in multiple simultaneous auctions for interacting goods. The core of our approach is learning a model of the empirical price dynamics based on past data and using the model to analytically calculate, to the greatest extent possible, optimal bids. This approach is fully implemented as ATTac-2001, a top-scoring agent in the second Trading Agent Competition (TAC-01). ATTac-2001 uses boosting techniques to learn conditional distributions of auction clearing prices. We present experiments demonstrating the effectiveness of this predictor relative to several reasonable alternatives.