Walverine: a Walrasian trading agent

  • Authors:

  • Shih-Fen Cheng;Evan Leung;Kevin M. Lochner;Kevin O'Malley;Daniel M. Reeves;L. Julian Schvartzman;Michael P. Wellman

  • Affiliations:

  • University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI

  • Venue:
  • AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
  • Year:
  • 2003

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Abstract

TAC-02 was the third in a series of Trading Agent Competition events fostering research in automating trading strategies by showcasing alternate approaches in an open-invitation market game. TAC presents a challenging travel-shopping scenario where agents must satisfy client preferences for complementary and substitutable goods by interacting through a variety of market types. Michigan's entry, Walverine, attempts to bid optimally based on a competitive analysis of the TAC travel economy. Walverine's approach embodies several techniques not previously employed in TAC: (1) price prediction based on competitive equilibrium analysis, (2) hedged optimization with respect to a model of outlier prices, (3) optimal bidding based on a decision-theoretic calculation of bid actions, and (4) reinforcement learning for CDA trading strategies. Each of these is potentially applicable in a broad class of trading environments.