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English(EN) Social Laws for Multi-agent Coordination in Stochastic Environments

揭示了随机多智能体系统社会法则的新框架

本文介绍了一个用于在随机环境中运行的多智能体系统的社会法则的新框架。它将先前从确定性环境中的工作扩展到基于奖励的场景,提出了一种定义和验证这些法则鲁棒性的方法。该研究引入了一个称为“alpha-鲁棒性”的概念,以量化遵守社会法则的智能体在追求其最优策略时保证的效用。作者提出了一种将问题简化为求解多个马尔可夫决策过程的验证方法,并通过实证评估展示了其潜力。 AI

影响 这项研究可能导致复杂的多智能体交互式AI系统中更鲁棒、更高效的协调。

排序理由 该集群包含一篇在arXiv上发表的学术论文,详细介绍了多智能体系统的新理论框架和验证方法。

在 arXiv cs.MA (Multiagent) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

揭示了随机多智能体系统社会法则的新框架

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该集群包含一篇在arXiv上发表的学术论文,详细介绍了多智能体系统的新理论框架和验证方法。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Rolando Fernandez, Caleb Probine, Tyler Lee, Jeffrey Chen, Erez Karpas, Muhammad Arrasy Rahman, Peter Stone, Ufuk Topcu ·

    随机环境下的多智能体协调的社会法则

    arXiv:2609.18929v1 Announce Type: cross Abstract: In multi-agent environments, coordinating agents to prevent interference and ensure robust individual performance is a critical challenge. Previous research on social laws for multi-agent systems has primarily focused on determini…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Ufuk Topcu ·

    随机环境下的多智能体协调的社会法则

    In multi-agent environments, coordinating agents to prevent interference and ensure robust individual performance is a critical challenge. Previous research on social laws for multi-agent systems has primarily focused on deterministic, goal-based settings. This paper extends the …