Peter Stone's Selected Publications

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Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions

Peter Stone, Robert E. Schapire, Michael L. Littman, János A. Csirik, and David McAllester. Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions. Journal of Artificial Intelligence Research, 19:209–242, 2003.
Available from journal's web page.

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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.

BibTeX Entry

@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="http://www.jair.org/papers/paper1200.html">journal's web page</a>.},
)

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