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Updated: Sep 12, 2025

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多个代理系统中的动态链路强度下的多重影响最大化:竞争和合作案例
IEEE transactions on neural networks and learning systems
|August 6, 2025
概括
本研究引入了多代理系统中动态链接强度的新模型,以优化多种影响最大化. 拟议的分布式深度强化学习框架提高了竞争和合作环境中的影响力扩散效率.
科学领域:
- 人工智能的人工智能
- 网络科学 网络科学
- 计算机科学 计算机科学
背景情况:
- 多代理系统 (MAS) 在理解影响传播方面存在复杂的挑战.
- 动态链接强度显著影响扩散过程,需要先进的建模.
- 优化影响扩散对于MAS的各种应用至关重要.
研究的目的:
- 提出一个新的模型,用于MAS的动态链接强度,以模拟多种影响的扩散.
- 在动态链路强度下 (MIMDLS) 制定多重影响最大化问题,考虑竞争和合作场景.
- 开发一个分布式的深度强化学习 (DRL) 框架,以在MIMDLS.中进行高效的种子选择.
主要方法:
- 一个新的动态链接强度模型用于MAS模拟多种影响的扩散.
- 两种扩散模型:竞争性的多重影响独立级联 (Cp-MIIC) 和合作性的多重影响线性值 (Cr-MILT).
- 一个分布式的DRL框架,为种子选择提供异步的培训和更新,包括Q值估计和约束管理.
主要成果:
- 验证了多重影响扩散的拟议模型和算法的有效性和效率.
- 与最先进的方法相比,开发的分布式DRL算法的表现优越.
- 展示了框架在处理竞争和合作影响力扩散场景方面的能力.
结论:
- 拟议的动态链接强度模型和DRL框架有效地解决了MIMDLS在MASS中的问题.
- Cp-MIIC和Cr-MILT模型提供了模拟竞争和合作影响力扩散的强有力的机制.
- 分布式DRL方法提供了一种高效且可扩展的解决方案,用于优化动态网络环境中的影响最大化.
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