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English(EN) Gossip Agents: Reputation through Opinion Dynamics over Evolving Networks

Gossip Agents 框架探索声誉扩散以促进多智能体系统中的合作

研究人员开发了一个名为 Gossip Agents 的新框架,用于研究声誉系统如何在共享环境中促进自利学习智能体之间的合作。该系统利用“八卦”(智能体交换有关第三方的信息)来扩散声誉数据,跨越动态通信网络。该研究探讨了信息传播范围的变化(从私人意见到全球声誉)如何影响囚徒困境和收获等社会困境情景下的合作。研究结果表明,声誉扩散的程度显著影响合作水平,并可能导致更可持续的行为。 AI

影响 这项研究可能导致多智能体系统中更具合作性和可持续性的行为,影响机器人技术和博弈论等领域。

排序理由 该集群包含一篇详细介绍多智能体系统新框架和研究结果的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.MA (Multiagent) 阅读 →

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Gossip Agents 框架探索声誉扩散以促进多智能体系统中的合作

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该集群包含一篇详细介绍多智能体系统新框架和研究结果的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Arlei Silva ·

    Gossip Agents: Reputation through Opinion Dynamics over Evolving Networks

    Sustained cooperation among self-interested learning agents is essential in many shared environments. Reputation systems are among the best-studied mechanisms for promoting such cooperation. Existing multi-agent reinforcement learning approaches, however, typically assume that re…