Strategy and mechanism lessons from the first ad auctions trading agent competition
Proceedings of the 11th ACM conference on Electronic commerce
TacTex09: a champion bidding agent for ad auctions
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
A Knapsack-Based Approach to Bidding in Ad Auctions
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
A particle filter for bid estimation in ad auctions with periodic ranking observations
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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We study the empirical behavior of trading agents participating in the Ad-Auction game of the Trading Agent Competition (TAC-AA). Aiming to understand the applicability of optimal trading strategies in synthesized environments to real-life settings, we investigate the robustness of the agents to deviations from the game's specified environment. Our results indicate that most agents, especially the top-scoring ones, are surprisingly robust. In addition, using the game logs, we derive for each agent a strategic fingerprint and show that it almost uniquely identifies it. Finally, we show that although the Machine Learning modeling in TAC-AA is inherently inaccurate, further improvement in modeling accuracy is likely to have only a limited contribution to the overall performance of TAC-AA agents.