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New algorithm computes exact equilibria in two-player zero-sum games

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a deterministic polynomial-time algorithm for calculating exact extensive-form proper equilibria in two-player zero-sum games with perfect recall. The algorithm assigns each information set a geometric permutahedron of action probabilities, which are then embedded into a polynomial-size sequence-form polytope using a doubly stochastic representation. This method ensures rational probabilities with polynomial encoding length, even for information sets with vanishing realization probabilities, by establishing sign stability of a parametric linear system and constructing a persistent feasible basis. AI

IMPACT This research advances computational methods in game theory, potentially impacting AI agents that operate in complex, multi-agent environments.

RANK_REASON The cluster contains a research paper detailing a new algorithm for computing equilibria in game theory.

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New algorithm computes exact equilibria in two-player zero-sum games

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Sam Ganzfried ·

    Polynomial-Time Computation of Extensive-Form Proper Equilibria in Two-Player Zero-Sum Games

    arXiv:2611.07089v1 Announce Type: cross Abstract: We give a deterministic polynomial-time algorithm for computing an exact extensive-form proper equilibrium in a finite two-player zero-sum extensive-form game with perfect recall and rational data. Properness is understood in the …

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Sam Ganzfried ·

    Polynomial-Time Computation of Extensive-Form Proper Equilibria in Two-Player Zero-Sum Games

    We give a deterministic polynomial-time algorithm for computing an exact extensive-form proper equilibrium in a finite two-player zero-sum extensive-form game with perfect recall and rational data. Properness is understood in the agent normal form: an action at an information set…

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