Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions
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-2001, 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.
Cite
Text
Stone et al. "Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions." Journal of Artificial Intelligence Research, 2003. doi:10.1613/JAIR.1200Markdown
[Stone et al. "Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions." Journal of Artificial Intelligence Research, 2003.](https://mlanthology.org/jair/2003/stone2003jair-decisiontheoretic/) doi:10.1613/JAIR.1200BibTeX
@article{stone2003jair-decisiontheoretic,
title = {{Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions}},
author = {Stone, Peter and Schapire, Robert E. and Littman, Michael L. and Csirik, János A. and McAllester, David A.},
journal = {Journal of Artificial Intelligence Research},
year = {2003},
pages = {209-242},
doi = {10.1613/JAIR.1200},
volume = {19},
url = {https://mlanthology.org/jair/2003/stone2003jair-decisiontheoretic/}
}