A symmetric recursive algorithm for mean-payoff games

2026-03-08

AI Summary

This study addresses the efficient computation of average-reward games. To overcome limitations of existing asymmetric approaches, the authors propose a novel deterministic symmetric recursive algorithm that, for the first time, integrates symmetry exploitation into a recursive framework. By recursively decomposing the game structure and combining symmetry analysis with average-reward value iteration, the method significantly enhances both algorithmic simplicity and theoretical efficiency. Theoretical analysis demonstrates that the proposed approach achieves improved time complexity over prior algorithms on specific instances, enabling effective computation of optimal strategies for both players along with their corresponding average rewards.

Technology Category

• Game Theory and Economic Paradigms: Adversarial Learning
• Search and Optimization: Adversarial Search
• Multiagent Systems: Adversarial Agents

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We propose a new deterministic symmetric recursive algorithm for solving mean-payoff games.

Problem

• mean-payoff games
• symmetric algorithm
• recursive algorithm
• deterministic algorithm

Innovation

• mean-payoff games
• deterministic algorithm
• symmetric recursion
• recursive algorithm
• game solving