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Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions (2003)
Peter Stone
, Robert E. Schapire, Michael L. Littman, J'anos A. Csirik, and David McAllester
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.
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Citation:
Journal of Artificial Intelligence Research
, Vol. 19 (2003), pp. 209-242.
Bibtex:
@article{JAIR-tac01, title={Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions}, author={Peter Stone and Robert E. Schapire and Michael L. Littman and J'anos A. Csirik and David McAllester}, volume={19}, journal={Journal of Artificial Intelligence Research}, pages={209-242}, url="http://www.cs.utexas.edu/users/ai-lab?JAIR-tac01", year={2003} }
People
Peter Stone
Faculty
pstone [at] cs utexas edu
Areas of Interest
Auctions
Machine Learning
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