Developing Decentralised Resilience to Malicious Influence in Collective Perception Problem

TL;DR

This paper presents a machine learning-based approach to enhance decentralised resilience in collective perception, enabling swarm agents to maintain performance despite malicious influence through dynamic opinion weighting.

Contribution

It introduces a curriculum inspired by Machine Education to train agents for resilience against malicious influence in decentralized collective decision-making.

Findings

Dynamic opinion weighting showed similar effectiveness to constant weights.

Momentum-based opinion fusion may inherently provide resilience.

Well-designed rules can produce effective resilient agents.

Abstract

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.