联盟如何在复杂网络上加强IPD合作

A. Peleteiro, J. C. Burguillo, Ana L. C. Bazzan
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引用次数: 12

摘要

将智能体组织成联盟是多智能体系统(MAS)提高性能、完成任务、增加效益和实现目标的重要合作方式。然而,属于联盟的代理人必须决定如何行动以增加他们的收益。在本文中,我们提出了一个结合学习和联盟形成的模型来增强迭代囚徒困境(IPD)中的合作。我们关注智能体在复杂网络上的交互,因为它们提供了当今互联世界的现实模型。我们的研究结果表明,联盟是实现IPD合作的相关贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How Coalitions Enhance Cooperation in the IPD over Complex Networks
Grouping agents into coalitions can be an important way of cooperation through which multi-agent systems(MAS) improve their performance, accomplish their assignments, increase their benefits, and achieve their goals. However, agents belonging to a coalition must decide how they behave to increase their gains. In this paper, we present a model that combines learning and coalition formation to enhance cooperation over the Iterated Prisoner's Dilemma (IPD). We focus on agents interacting over complex networks since they provide a realistic model of the nowadays interconnected world. Our results suggest that coalitions are a relevant contribution to achieve cooperation in the IPD.
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