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.

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In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), May 2010.

@InProceedings{AAMAS10-pardoe, title={TacTex09: A Champion Bidding Agent for Ad Auctions}, author={David Pardoe and Doran Chakraborty and Peter Stone}, booktitle={Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010)}, month={May}, url="http://nn.cs.utexas.edu/?pardoe:AAMAS10", year={2010} }