  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.
