论文标题
在集体感知问题中发展对恶意影响的分散弹性
Developing Decentralised Resilience to Malicious Influence in Collective Perception Problem
论文作者
论文摘要
在集体决策中，设计仅使用本地信息来实现群体行为的算法是一个非平凡的问题。我们使用机器学习技术来教群成员将其对环境的当地看法绘制为最佳行动。受机器教育方法启发的课程旨在促进这一学习过程，并教会成员在集体感知问题中最佳表现所需的技能。我们通过创建一种教师对恶意影响力的韧性的课程来扩展了以前的方法。实验结果表明，精心设计的基于规则的算法可以产生有效的药物。在执行意见融合时，我们通过动态体重收到意见来实现分散的弹性。我们发现恒定和动态权重之间存在非显着的差异，这表明基于动量的意见融合也许已经是一种弹性机制。
In collective decision-making, designing algorithms that use only local information to effect swarm-level behaviour is a non-trivial problem. We used machine learning techniques to teach swarm members to map their local perceptions of the environment to an optimal action. A curriculum inspired by Machine Education approaches was designed to facilitate this learning process and teach the members the skills required for optimal performance in the collective perception problem. We extended upon previous approaches by creating a curriculum that taught agents resilience to malicious influence. The experimental results show that well-designed rules-based algorithms can produce effective agents. When performing opinion fusion, we implemented decentralised resilience by having agents dynamically weight received opinion. We found a non-significant difference between constant and dynamic weights, suggesting that momentum-based opinion fusion is perhaps already a resilience mechanism.
