通过合作交流策略进行异质的多代理任务分配,以改善平衡:潜在的游戏框架
IEEE transactions on cybernetics
|November 4, 2025
概括
本研究介绍了一种用于多代理任务分配的联盟形成游戏,提高了超过50%的效率. 一个先进的学习算法有助于在动态场景 (如灾难响应) 中逃避次优解决方案.
科学领域:
- 多代理系统 多代理系统
- 游戏理论 游戏理论
- 运营研究 运营研究
背景情况:
- 由于代理和任务异质性,多代理系统中的任务分配是复杂的.
- 资源枯竭和动态任务要求增加了进一步的挑战.
- 应用包括优化无人机部署用于灾难救援.
研究的目的:
- 为有效的任务分配提出一个联盟形成游戏模型.
- 从理论上分析游戏的特性和效率的界限.
- 开发一种学习算法,以提高全球效用,避免低于最佳的平衡.
主要方法:
- 制定一个联盟形成游戏作为一个潜在的游戏.
- 对于联盟函数和全球实用函数的子模块化的理论证明.
- 开发一个基于惯性的日志线性学习算法与合作交换.
- 扩展本地通信和动态场景的算法.
主要成果:
- 纳什平衡的理论效率下限为 $e / (2e-1) $,超过了以前的50%的边界.
- 学习算法有效地逃脱了低于最佳的平衡,并提高了全球效用.
- 在实验评估中,在各种场景中表现出卓越的性能.
结论:
- 拟议的联盟形成游戏和学习算法在多代理任务分配方面取得了重大进展.
- 理论框架保证了提高效率的极限.
- 实际的算法在复杂的,动态的环境中表现出强度和有效性.
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