Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
Exploiting scale invariant dynamics for efficient information propagation in large teams
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
An investigation of the vulnerabilities of scale invariant dynamics in large teams
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Dynamic facts in large team information sharing
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Active sensing in complex multiagent environments
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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In this paper we present an approach for improving the accuracy of shared opinions in a large decentralised team. Specifically, our solution optimises the opinion sharing process in order to help the majority of agents to form the correct opinion about a state of a common subject of interest, given only few agents with noisy sensors in the large team. We build on existing research that has examined models of this opinion sharing problem and shown the existence of optimal parameters where incorrect opinions are filtered out during the sharing process. In order to exploit this collective behaviour in complex networks, we present a new decentralised algorithm that allows each agent to gradually regulate the importance of its neighbours' opinions (their social influence). This leads the system to the optimised state in which agents are most likely to filter incorrect opinions, and form a correct opinion regarding the subject of interest. Crucially, our algorithm is the first that does not introduce additional communication over the opinion sharing itself. Using it 80-90% of the agents form the correct opinion, in contrast to 60-75% with the existing message-passing algorithm DACOR proposed for this setting. Moreover, our solution is adaptive to the network topology and scales to thousands of agents. Finally, the use of our algorithm allows agents to significantly improve their accuracy even when deployed by only half of the team.