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      "statement": "# DeepStack: continual re-solving with learned counterfactual values\n\nCombined continual re-solving, learned counterfactual value functions and depth/action-limited search for heads-up no-limit poker, demonstrating professional-player performance in the reported study.\n\n## Scientific scope\n\nDistinct from visual-token DeepStack. Source soundness theorem assumes bounded value-function error and adequate CFR iterations; the practical sparse action-tree restriction explicitly voids that theorem's guarantee. Thus professional-match results are empirical, not a certified exact Nash solution or zero exploitability. CFR and earlier re-solving are inherited.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nMatej Moravčík; Martin Schmid; Neil Burch; Viliam Lisý; Dustin Morrill; Nolan Bard; Trevor Davis; Kevin Waugh; Michael Johanson; Michael Bowling\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [DeepStack: Expert-Level Artificial Intelligence in Heads-Up No-Limit Poker](https://poker.cs.ualberta.ca/publications/17science.pdf)\n",
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