Representations and solutions for game-theoretic problems
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Computing an approximate jam/fold equilibrium for 3-player no-limit Texas Hold'em tournaments
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Abstraction pathologies in extensive games
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Approximating game-theoretic optimal strategies for full-scale poker
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Computing equilibria in multiplayer stochastic games of imperfect information
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Probabilistic state translation in extensive games with large action sets
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Gradient-based algorithms for finding Nash equilibria in extensive form games
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Artificial Intelligence
Computing approximate Nash Equilibria and robust best-responses using sampling
Journal of Artificial Intelligence Research
Regret minimization in multiplayer extensive games
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Case-based strategies in computer poker
AI Communications
A parameterized family of equilibrium profiles for three-player kuhn poker
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Evaluating state-space abstractions in extensive-form games
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Games are used to evaluate and advance Multiagent and Artificial Intelligence techniques. Most of these games are deterministic with perfect information (e.g. Chess and Checkers). A deterministic game has no chance element and in a perfect information game, all information is visible to all players. However, many real-world scenarios with competing agents are stochastic (non-deterministic) with imperfect information. For two-player zero-sum perfect recall games, a recent technique called Counterfactual Regret Minimization (CFR) computes strategies that are provably convergent to an ε-Nash equilibrium. A Nash equilibrium strategy is useful in two-player games since it maximizes its utility against a worst-case opponent. However, for multiplayer (three or more player) games, we lose all theoretical guarantees for CFR. However, we believe that CFR-generated agents may perform well in multiplayer games. To test this hypothesis, we used this technique to create several 3-player limit Texas Hold'em poker agents and two of them placed first and second in the 3-player event of the 2009 AAAI/IJCAI Computer Poker Competition. We also demonstrate that good strategies can be obtained by grafting sets of two-player subgame strategies to a 3-player base strategy after one of the players is eliminated.