S-F Cheng, E Leung, KM Lochner, K O’Malley, DM Reeves, LJ Schvartzman, and MP Wellman

Decision Support Systems 39:169–184, 2005
Copyright © 2003 Elsevier B.V. All rights reserved.

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.

An earlier version appeared in Second International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-03), pages 465-472, Melbourne, July 2003.

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