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The HoneyComb Paradigm for Research on Collective Human Behavior
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自私的联盟群众感知多代理互动环境监控
Xiuwen Liu1, Xinghua Lei1, Xin Li1
1College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
这项研究引入了移动人群传感 (MCS) 的新框架,以改善环境监测. 自利联盟人群传感 (SCC-MIE) 方法提高了数据准确性,并在复杂的传感环境中降低了成本.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 移动人群传感 (MCS) 利用分布式传感能力为大型服务,如智能运输和环境监测.
- 多代理强化学习 (MARL) 策略培训需要广泛的环境相互作用,导致高成本.
- 复杂的传感环境产生稀疏,异质的数据,阻碍了准确的环境重建.
研究的目的:
- 开发一个强大的多代理环境监测框架 (SCC-MIE),以应对数据稀疏性和异质性的挑战.
- 提高MCS环境重建和工人选择的准确性和效率.
- 为了降低与MCS的MARL战略培训相关的成本.
主要方法:
- 在多代理生成对抗模仿学习框架内开发了一个自我感兴趣的联盟学习策略.
- 集成了一个重建器和区分器,用于协作学习传感环境和隐藏的混因素.
- 雇佣了秘书问题,以便实时选择最佳的员工进行数据收集.
主要成果:
- 与现有模型相比,SCC-MIE框架显示了环境监测的显著性能改善.
- 该方法有效地处理稀疏和异质的数据,以便更准确地重建环境.
- 通过合作学习实现了环境监测结果的增强解释性.
结论:
- SCC-MIE提供了一种强大且具有成本效益的解决方案,用于使用MCS.
- 拟议的自利联盟学习策略增强了合作和学习准确性.
- 这一框架为智能环境和数据驱动服务中的先进应用提供了一个有希望的方向.
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