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
@article{arxiv.1106.5270,
title = {Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions},
author = {J. A. Csirik and M. L. Littman and D. McAllester and R. E. Schapire and P. Stone},
journal= {arXiv preprint arXiv:1106.5270},
year = {2011}
}