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- W2806893583 abstract "Standard subdivision techniques, like backwards induction, are successful in perfect-information games; however, in imperfect-information games they have no theoretical justification. Despite this lack of theory, the strongest agents for large imperfect-information games have employed endgame solving'' and have recently beaten human professional players in poker using this approach. We present an agent that uses a modification, which can be viewed as a generalization of endgame solving, called midgame solving. While endgame solving solves the final portion of a game, assuming strategies prior to the endgame as inputs using Bayes' rule, our approach solves an arbitrary portion of the game, which could be an intermediate portion with further play remaining subsequently. One of the main challenges is determining the payoffs to use for the midgame, while terminal payoffs are used for endgames. We also present a novel approach for interpreting opponents' off-tree actions that is more robust than prior approaches. We describe an agent for no-limit Texas hold 'em utilizing our approaches and preliminary experiments against an agent from the Annual Computer Poker Competition that uses popular state-of-the-art techniques such as CFR+." @default.
- W2806893583 created "2018-06-13" @default.
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- W2806893583 date "2017-11-01" @default.
- W2806893583 modified "2023-09-24" @default.
- W2806893583 title "Midgame Solving: A New Weapon for Efficient Large-Scale Equilibrium Approximation" @default.
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- W2806893583 doi "https://doi.org/10.1109/ictai.2017.00069" @default.
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