Keyword auctions are becoming increasingly important in today's electronic marketplaces. One of their most challenging aspects is the limited amount of information revealed about other advertisers. In this paper, we present a particle filter that can be used to estimate the bids of other advertisers given a periodic ranking of their bids. This particle filter makes use of models of the bidding behavior of other advertisers, and so we also show how such models can be learned from past bidding data. In experiments in the Ad Auction scenario of the Trading Agent Competition, the combination of this particle filter and bidder modeling outperforms all other bid estimation methods tested.

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In Proc. of 10th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2011), May 2011.

@inproceedings{AAMAS11-pardoe, title={A Particle Filter for Bid Estimation in Ad Auctions with Periodic Ranking Observations}, author={David Pardoe and Peter Stone}, booktitle={Proc. of 10th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2011)}, month={May}, url="http://www.cs.utexas.edu/users/ai-lab?AAMAS11-pardoe", year={2011} }