mlsanityData to 14 Oct 2026

Observable Perfect Equilibrium

arXiv 2210.16506 ↗First posted 29 Oct 2022cs.GT cs.AI cs.MAcited by 0

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Abstract (arXiv)

While Nash equilibrium has emerged as the central game-theoretic solution concept, many important games contain several Nash equilibria and we must determine how to select between them in order to create real strategic agents. Several Nash equilibrium refinement concepts have been proposed and studied for sequential imperfect-information games, the most prominent being trembling-hand perfect equilibrium, quasi-perfect equilibrium, and recently one-sided quasi-perfect equilibrium. These concepts are robust to certain arbitrarily small mistakes, and are guaranteed to always exist; however, we argue that neither of these is the correct concept for developing strong agents in sequential games of imperfect information. We define a new equilibrium refinement concept for extensive-form games called observable perfect equilibrium in which the solution is robust over trembles in publicly-observable action probabilities (not necessarily over all action probabilities that may not be observable by opposing players). Observable perfect equilibrium correctly captures the assumption that the opponent is playing as rationally as possible given mistakes that have been observed (while previous solution concepts do not). We prove that observable perfect equilibrium is always guaranteed to exist, and demonstrate that it leads to a different solution than the prior extensive-form refinements in no-limit poker. We expect observable perfect equilibrium to be a useful equilibrium refinement concept for modeling many important imperfect-information games of interest in artificial intelligence.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 32 references that are in the map. First posted on arXiv 29 Oct 2022.

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References · 2

  1. 2017DeepStack: Expert-Level Artificial Intelligence in No-Limit Pokercited by 233
  2. 2016Settling the complexity of computing approximate two-player Nash equilibriacited by 31

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