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.

翻译：在集体决策中,设计仅使用当地信息来影响群集层面行为的算法是一个非三重问题。我们使用机器学习技术来教育群集成员将当地对环境的看法映射为最佳行动。由机器教育方法所启发的课程设计是为了促进这一学习过程,并教授成员在集体认识问题中最佳表现所需的技能。我们根据以往的方法,设计了一种课程,教授代理人如何抵御恶意影响。实验结果显示,精心设计的基于规则的算法可以产生有效的代理物。在进行意见融合时,我们通过让代理人动态地接受意见而实行了分散的适应力。我们发现,常态和动态的权重之间没有多大区别,这表明基于动力的意见融合或许已经是一种复原机制。