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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.
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="https://www.jair.org/index.php/jair/article/view/10339">journal's web page</a>.},
)
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