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	Regret Minimization in Games with Incomplete Information Martin Zinkevich Michael Johanson Add to Reading List Document Date: 2007-09-26 13:24:43 Open Document File Size: 231,98 KB Share Result on Facebook City Cambridge / / Company Neural Information Processing Systems / MIT Press / Michael Bowling Computing Science Department University of Alberta Edmonton AB / Carmelo Piccione Computing Science Department University of Alberta Edmonton AB / / / Event Product Recall / Product Issues / / IndustryTerm Gradient-based algorithms / benchmark server / regret matching algorithm / recent state-of-the-art algorithms / regret algorithms / No-regret algorithms / considerable additional technology / near equilibrium solution / equilibrium solution / regret minimizing algorithms / e - commerce / pokerspecific algorithm / approximate solutions / learning algorithms / online learning concept / / Organization H\Z / Poker Academy / MIT / University of Alberta Edmonton / / Person Regret Extensive / Nash Equilibria / Ai / Martin Zinkevich / Michael Johanson / / Position first player / competitive texas hold’em poker player / chance player / associated player / controlling player / If player / Rt / row player / player / overall value Pto player / max Rt / / Product 0 Because the game / Canada T6G2E8 / / ProvinceOrState Massachusetts / / PublishedMedium Games and Economic Behavior / / Technology regret algorithms / regret matching algorithm / four processors / Lagrangian Hedging algorithms / Gradient-based algorithms / No-regret algorithms / 280 processor / regret minimizing algorithms / / SocialTag Nash equilibrium Extensive-form game Determinacy Normal distribution Structure Trembling hand perfect equilibrium Epsilon-equilibrium Decision theory Game theory Problem solving