Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
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
Gradient-based algorithms for finding Nash equilibria in extensive form games
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Using counterfactual regret minimization to create competitive multiplayer poker agents
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Artificial Intelligence
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Successful performance via decision generalisation in no limit texas hold'em
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
Lossy stochastic game abstraction with bounds
Proceedings of the 13th ACM Conference on Electronic Commerce
Strategy purification and thresholding: effective non-equilibrium approaches for playing large games
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Case-based strategies in computer poker
AI Communications
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Decision generalisation from game logs in no limit texas Hold'em
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Equilibrium or near-equilibrium solutions to very large extensive form games are often computed by using abstractions to reduce the game size. A common abstraction technique for games with a large number of available actions is to restrict the number of legal actions in every state. This method has been used to discover equilibrium solutions for the game of no-limit heads-up Texas Hold'em. When using a solution to an abstracted game to play one side in the un-abstracted (real) game, the real opponent actions may not correspond to actions in the abstracted game. The most popular method for handling this situation is to translate opponent actions in the real game to the closest legal actions in the abstracted game. We show that this approach can result in a very exploitable player and propose an alternative solution. We use probabilistic mapping to translate a real action into a probability distribution over actions, whose weights are determined by a similarity metric. We show that this approach significantly reduces the exploitability when using an abstract solution in the real game.