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Revenue Maximising Adaptive Auctioneer Agent

  • Janine Claire Pike
  • Elizabeth Marie Ehlers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5357)

Abstract

Auction theory has proven that auction revenue is influenced by factors such as the auction format and the auction parameters. The Revenue Maximising Adaptive Auctioneer (RMAA) agent model has been developed with the aim of generating maximum auction revenue by adapting the auction format and parameters to suit the auction environment. The RMAA agent uses a learning classifier system to learn which rules are profitable in a particular bidding environment. The profitable rules are then exploited by the RMAA agent to generate maximum revenue. The RMAA agent model can effectively adapt to a real time dynamic auction environment.

Keywords

Agent auctions auction theory reinforcement learning learning classifier system ZCS 

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References

  1. 1.
    Gerding, E.H., Rogers, A., Dash, R.K., Jennings, N.R.: Competing Sellers in Online Markets: Reserve Prices, Shill bidding, and Auction Fees. In: 5th International Joint Conference on Autonomous agents and Multiagent Systems, New York, pp. 1208–1210 (2006)Google Scholar
  2. 2.
    Milgrom, P.: Putting Auction Theory to Work. Cambridge University Press, Cambridge (2004)CrossRefGoogle Scholar
  3. 3.
    Klemperer, P.: Auction Theory: A Guide to the Literature, Technical report, EconWPA (1999)Google Scholar
  4. 4.
    Monderer, D., Tennenholtz, M.: Optimal Auctions Revisited. Faculty of Industrial Engineering Technion – Israel Institute of Technology (1998)Google Scholar
  5. 5.
    Sandholm, T.: Distributed Rational Decision Making in Multiagent Systems. In: Weiss, G. (ed.), ch. 5. MIT Press, Cambridge (1999)Google Scholar
  6. 6.
    Lucking-Reiley, D.: Using Field Experiments to Test Equivalence between Auction Formats: Magic on the Internet. The American Economic Review 89(5), 1063–1080 (1999)CrossRefGoogle Scholar
  7. 7.
    Bryan, D., Lucking-Reiley, D., Prasad, N., Reeves, D.: Pennies from eBay: the Determinants of Price in Online Auctions. Econometric Society World Congress 2000 Contributed Papers 1736, Econometric Society (2000)Google Scholar
  8. 8.
    Onur, I., Tomak, K.: Impact of ending rules in online auctions: the case of Yahoo.com. Decis. Support Syst. 42(3), 1835–1842 (2006)CrossRefGoogle Scholar
  9. 9.
    Gregg, D.G., Walczak, S.: Auction Advisor: an agent-based online-auction decision support system. Decis. Support Syst. 41(2), 449–471 (2006)CrossRefGoogle Scholar
  10. 10.
    Pardoe, D., Stone, P.: Developing Adaptive Auction Mechanisms. SIGecom Exch. 5(3), 1–10 (2005)CrossRefGoogle Scholar
  11. 11.
    Cliff, D.: Evolution of Market Mechanism Through a Continuous Space of Auction-Types. In: IEEE Congress on Evolutionary Computation on 2002, Washington, DC, USA, pp. 2029–2034 (2002)Google Scholar
  12. 12.
    Wilson, S.W.: ZCS: A Zeroth Level Classifier System. Evol. Comput. 2(1), 1–18 (1994)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Zhang, J., Zhang, N., Chung, J.: Assisting Seller Pricing Strategy Selection for Electronic Auctions. In: IEEE International Conference on E-Commerce Technology, pp. 27–33. IEEE Computer Society, Washington (2004)Google Scholar
  14. 14.
    Vermorel, J., Mohry, M.: Multi-armed Bandit Algorithms and Empirical Evaluation. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS, vol. 3720, pp. 437–448. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Janine Claire Pike
    • 1
  • Elizabeth Marie Ehlers
    • 1
  1. 1.Academy for Information TechnologyUniversity of JohannesburgJohannesburgSouth Africa

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