Poker AI: Algorithms for Creating Game-Theoretic Strategies for Large Incomplete-Information Games

author: Tuomas Sandholm, Computer Science Department, Carnegie Mellon University
published: Nov. 14, 2013,   recorded: July 2013,   views: 3710
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Description

Incomplete-information games - such as most auctions, negotiations, and future (cyber)security settings - cannot be solved using minimax search even in principle. Completely different algorithms were needed. A dramatic scalability leap has occurred in our ability to solve such games over the last seven years, fueled largely by the Annual Computer Poker Competition. I will discuss the key domain-independent techniques that enabled this leap, including automated abstraction techniques and approaches for mitigating the issues that they raise, new equilibrium-finding algorithms, safe opponent exploitation methods, techniques that use qualitative knowledge as an extra input, and endgame solving techniques. I will finish by benchmarking poker programs against the best human poker professionals and by discussing what humans can learn from the programs.

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