Peter Stone's Selected Publications

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A Particle Filter for Bid Estimation in Ad Auctions with Periodic Ranking Observations

David Pardoe and Peter Stone. A Particle Filter for Bid Estimation in Ad Auctions with Periodic Ranking Observations. In Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2011.

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Abstract

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.

BibTeX Entry

@InProceedings{AAMAS11-pardoe,
  author = {David Pardoe and Peter Stone},
  title = {A Particle Filter for Bid Estimation in Ad Auctions with Periodic Ranking Observations},
  booktitle = {Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
  location = {Taipei, Taiwan},
  month = {May},
  year = {2011},
  abstract = {
    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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