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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.
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.
@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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