Searching for Walverine 2005

  • Michael P. Wellman
  • Daniel M. Reeves
  • Kevin M. Lochner
  • Rahul Suri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3937)


We systematically explore a range of variations of our TAC travel-shopping agent, Walverine. The space of strategies is defined by settings to behavioral parameter values. Our empirical game-theoretic analysis is facilitated by approximating games through hierarchical reduction methods. This approach generated a small set of candidates for the version to run in the TAC-05 tournament. We selected among these based on performance in preliminary rounds, ultimately identifying a successful strategy for Walverine 2005.


Strategic Interaction Adjusted Score Original Game Candidate Strategy Symmetric Game 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Cheng, S.F., Leung, E., Lochner, K.M., O’Malley, K., Reeves, D.M., Wellman, M.P.: Walverine: A Walrasian trading agent. Decision Support Systems 39, 169–184 (2005)CrossRefGoogle Scholar
  2. 2.
    Wellman, M.P., Reeves, D.M., Lochner, K.M., Vorobeychik, Y.: Price prediction in a trading agent competition. Journal of Artificial Intelligence Research 21, 19–36 (2004)Google Scholar
  3. 3.
    Greenwald, A., Boyan, J.: Bidding under uncertainty: Theory and experiments. In: Twentieth Conference on Uncertainty in Artificial Intelligence, Banff, pp. 209–216 (2004)Google Scholar
  4. 4.
    Vetsikas, I.A., Selman, B.: A principled study of the design tradeoffs for autonomous trading agents. In: Second International Joint Conference on Autonomous Agents and Multi-Agent Systems, Melbourne, pp. 473–480 (2003)Google Scholar
  5. 5.
    Wellman, M.P., Reeves, D.M., Lochner, K.M., Cheng, S.F., Suri, R.: Approximate strategic reasoning through hierarchical reduction of large symmetric games. In: Twentieth National Conference on Artificial Intelligence, Pittsburgh (2005)Google Scholar
  6. 6.
    Fritschi, C., Dorer, K.: Agent-oriented software engineering for successful TAC participation. In: First International Joint Conference on Autonomous Agents and Multi-Agent Systems, Bologna (2002)Google Scholar
  7. 7.
    Stone, P., Schapire, R.E., Littman, M.L., Csirik, J.A., McAllester, D.: Decision-theoretic bidding based on learned density models in simultaneous, interacting auctions. Journal of Artificial Intelligence Research 19, 209–242 (2003)MathSciNetMATHGoogle Scholar
  8. 8.
    Boyan, J., Greenwald, A.: Bid determination in simultaneous auctions: An agent architecture. In: Third ACM Conference on Electronic Commerce, Tampa, FL, pp. 210–212 (2001)Google Scholar
  9. 9.
    Greenwald, A., Stone, P.: The first international trading agent competition: Autonomous bidding agents. IEEE Internet Computing 5, 52–60 (2001)CrossRefGoogle Scholar
  10. 10.
    Stone, P., Littman, M.L., Singh, S., Kearns, M.: ATTac 2000: An adaptive autonomous bidding agent. Journal of Artificial Intelligence Research 15, 189–206 (2001)CrossRefMATHGoogle Scholar
  11. 11.
    Wellman, M.P., Greenwald, A., Stone, P., Wurman, P.R.: The 2001 trading agent competition. Electronic Markets 13, 4–12 (2003)CrossRefGoogle Scholar
  12. 12.
    He, M., Jennings, N.R.: SouthamptonTAC: Designing a successful trading agent. In: Fifteenth European Conference on Artificial Intelligence, Lyon, pp. 8–12 (2002)Google Scholar
  13. 13.
    He, M., Jennings, N.R.: SouthamptonTAC: An adaptive autonomous trading agent. ACM Transactions on Internet Technology 3, 218–235 (2003)CrossRefGoogle Scholar
  14. 14.
    Wellman, M.P., Cheng, S.F., Reeves, D.M., Lochner, K.M.: Trading agents competing: Performance, progress, and market effectiveness. IEEE Intelligent Systems 18, 48–53 (2003)CrossRefGoogle Scholar
  15. 15.
    Ross, S.M.: Simulation, 3rd edn. Academic Press, London (2002)Google Scholar
  16. 16.
    L’Ecuyer, P.: Efficiency improvement and variance reduction. In: Twenty-Sixth Winter Simulation Conference, Orlando, FL, pp. 122–132 (1994)Google Scholar
  17. 17.
    McKelvey, R.D., McLennan, A., Turocy, T.: Gambit game theory analysis software and tools (1992),

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Michael P. Wellman
    • 1
  • Daniel M. Reeves
    • 1
  • Kevin M. Lochner
    • 1
  • Rahul Suri
    • 1
  1. 1.University of MichiganAnn ArborUSA

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