This article aims to exemplify best-response dynamics and multi-agent online learning by group formation. This extended abstract provides a summary of the full paper in IEEE Computational Intelligence Magazine on the special issue AI-eXplained (AI-X). The full paper includes interactive components to facilitate interested readers to grasp the idea of pure-strategy Nash equilibria and how the system of strategic agents converges to a stable state by the decentralized online gradient ascent with and without regularization.

中文翻译：

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通过群组加入形成群组并通过多智能体在线梯度上升进行意见更新 [AI-eXplained]


本文旨在通过小组形成来举例说明最佳响应动态和多智能体在线学习。此扩展摘要提供了 IEEE 计算智能杂志上关于特刊 AI-eXplained (AI-X) 的全文的摘要。全文包含互动部分，以帮助感兴趣的读者掌握纯策略纳什均衡的概念，以及策略代理系统如何通过有或没有正则化的去中心化在线梯度上升收敛到稳定状态。