Uncertainty in Artificial Intelligence
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Computing Best-Response Strategies in Infinite Games of Incomplete Information
Daniel Reeves, Michael Wellman
We describe an algorithm for computing best response strategies in a class of two-player infinite games of incomplete information, defined by payoffs piecewise linear in agents' types and actions, conditional on linear comparisons of agents' actions. We show that this class includes many well-known games including a variety of auctions and a novel allocation game. In some cases, the best-response algorithm can be iterated to compute Bayes-Nash equilibria. We demonstrate the efficiency of our approach on existing and new games.
Keywords: null
Pages: 470-478
PS Link:
PDF Link: /papers/04/p470-reeves.pdf
AUTHOR = "Daniel Reeves and Michael Wellman",
TITLE = "Computing Best-Response Strategies in Infinite Games of Incomplete Information",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "470--478"

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