Developing Decentralised Resilience to Malicious Influence in Collective Perception Problem

Autor:	Wise, Chris, Hussein, Aya, El-Fiqi, Heba
Rok vydání:	2022
Předmět:	Computer Science - Multiagent Systems Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 14 Pages, 14 Figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.03063 Zobrazit plný text záznamu View this record from Arxiv