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
摘要：
在集体决策中，设计只使用局部信息来影响群体行为的算法是一个重要的问题。我们使用了机器学习技术来教会群体成员将他们对环境的局部感知映射到最佳行动。受机器教育方法启发的课程旨在促进这一学习过程，并教授成员在集体感知问题中获得最佳表现所需的技能。我们通过创建一个课程来扩展以前的方法，该课程教授代理对恶意影响的弹性。实验结果表明，设计良好的基于规则的算法可以产生有效的代理。在进行意见融合时，我们通过让代理动态加权收到的意见来实现分散的弹性。我们发现恒定权重和动态权重之间没有显著差异，这表明基于动量的意见融合可能已经是一种弹性机制。
英文摘要：
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.
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