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@article(JAIR-tac01,
    Author={Peter Stone and Robert E.~Schapire and Michael L.~Littman and J\'{a}nos A.~Csirik and David McAllester},
    Title="Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions",
    Journal="Journal of Artificial Intelligence Research",
    Year="2003",volume="19",pages="209--242",
    abstract={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, 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.},
    wwwnote = {Available from <a href="https://www.jair.org/index.php/jair/article/view/10339">journal's web page</a>.},
)