Abstract
We describe a model-free approach to bidding in the Ad-Auctions Trading Agents Competition: First, a simple and robust yet high-performing agent using a Regret Minimization optimization algorithm for the 2010 competition, followed by our top performing agent for the 2011 competition, still using simplified modeling and optimization methods. Specifically, we model the user populations using particle filters, but base the observations on a Nearest Neighbor estimator (instead of game specific parameters). We implement a simple and effective bid optimization algorithm by applying the equimarginal principle combined with perplexity-based regularization. The implementation of our 2011 agent also remains model-free in the sense that we do not attempt to model the competing agents behavior for estimating costs and associated game parameters.
This research was supported in part by the Google Inter-university center for Electronic Markets and Auctions, by a grant from the Israel Science Foundation, by a grant from United States-Israel Binational Science Foundation (BSF), by a grant from the Israeli Ministry of Science (MoS), and by the Israeli Centers of Research Excellence (I-CORE) program, (Center No. 4/11).
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Schain, M., Hertz, S., Mansour, Y. (2013). A Model-Free Approach for a TAC-AA Trading Agent. In: David, E., Kiekintveld, C., Robu, V., Shehory, O., Stein, S. (eds) Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets. AMEC TADA 2012 2012. Lecture Notes in Business Information Processing, vol 136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40864-9_9
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DOI: https://doi.org/10.1007/978-3-642-40864-9_9
Publisher Name: Springer, Berlin, Heidelberg
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