Autonomous Bidding Agents: Strategies and Lessons from the Trading Agent Competition (Intelligent Robotics and Autonomous Agents)
Strategy and mechanism lessons from the first ad auctions trading agent competition
Proceedings of the 11th ACM conference on Electronic commerce
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
An empirical study of trading agent robustness
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Multiagent learning in the presence of memory-bounded agents
Autonomous Agents and Multi-Agent Systems
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In the Trading Agent Competition Ad Auctions Game, agents compete to sell products by bidding to have their ads shown in a search engine's sponsored search results. We report on the winning agent from the first (2009) competition, TacTex. TacTex operates by estimating the full game state from limited information, using these estimates to make predictions, and then optimizing its actions (daily bids, ads, and spending limits) with respect to these predictions. We present a full description of TacTex along with analysis of its performance in both the competition and controlled experiments.