Searching for walverine 2005

Authors:
Michael P. Wellman;Daniel M. Reeves;Kevin M. Lochner;Rahul Suri
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
Venue:
AMEC'05 Proceedings of the 2005 international conference on Agent-Mediated Electronic Commerce: designing Trading Agents and Mechanisms
Year:
2005

Citing 14
Cited 2

Efficiency improvement and variance reduction

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Bid determination in simultaneous actions an agent architecture

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Simulation

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Agent-oriented software engineering for successful TAC participation

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Autonomous Bidding Agents in the Trading Agent Competition

IEEE Internet Computing
SouthamptonTAC: An adaptive autonomous trading agent

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A principled study of the design tradeoffs for autonomous trading agents

AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Bidding under uncertainty: theory and experiments

UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Walverine: a Walrasian trading agent

Decision Support Systems - Special issue: Decision theory and game theory in agent design
Trading Agents Competing: Performance, Progress, and Market Effectiveness

IEEE Intelligent Systems
Approximate strategic reasoning through hierarchical reduction of large symmetric games

AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Decision-theoretic bidding based on learned density models in simultaneous, interacting auctions

Journal of Artificial Intelligence Research
Price prediction in a trading agent competition

Journal of Artificial Intelligence Research
ATTac-2000: an adaptive autonomous bidding agent

Journal of Artificial Intelligence Research

Generalization risk minimization in empirical game models

Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
RoxyBot-06: stochastic prediction and optimization in TAC travel

Journal of Artificial Intelligence Research

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Abstract

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.