What changed
Decomposed overall strategic regret into information-set counterfactual regrets, enabling locally maintained regret-matching updates and approximate-equilibrium computation in large imperfect-information games.
Scientific scope
Decomposed overall strategic regret into information-set counterfactual regrets, enabling locally maintained regret-matching updates and approximate-equilibrium computation in large imperfect-information games. Finite perfect-recall extensive games underpin the stated regret decomposition; average self-play strategies yield the equilibrium guarantee in the two-player zero-sum setting. It is not a universal Nash-convergence theorem for arbitrary multiplayer/general-sum learning or unrestricted imperfect-recall abstractions. Blackwell approachability/regret matching are inherited. Source theorem statements read, proofs not independently replayed.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Finite perfect-recall extensive games underpin the stated regret decomposition; average self-play strategies yield the equilibrium guarantee in the two-player zero-sum setting. It is not a universal Nash-convergence theorem for arbitrary multiplayer/general-sum learning or unrestricted imperfect-recall abstractions. Blackwell approachability/regret matching are inherited. Source theorem statements read, proofs not independently replayed.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-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.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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