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Efficient Opinion Sharing in Large Decentralised Teams

Efficient Opinion Sharing in Large Decentralised Teams
Efficient Opinion Sharing in Large Decentralised Teams
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.
Self-organisation, Emergent behaviour, Distributed problem solving
543-550
Pryymak, Oleksandr
ab0ecf03-22c7-4d05-938b-5d41f98cb6b6
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Pryymak, Oleksandr
ab0ecf03-22c7-4d05-938b-5d41f98cb6b6
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Pryymak, Oleksandr, Rogers, Alex and Jennings, Nick (2012) Efficient Opinion Sharing in Large Decentralised Teams. Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), Spain. pp. 543-550 .

Record type: Conference or Workshop Item (Paper)

Abstract

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.

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Published date: 2012
Venue - Dates: Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), Spain, 2012-01-01
Keywords: Self-organisation, Emergent behaviour, Distributed problem solving
Organisations: Agents, Interactions & Complexity

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Local EPrints ID: 273087
URI: https://eprints.soton.ac.uk/id/eprint/273087
PURE UUID: 9f87d9a0-85da-44a9-907d-d7fc2252707d

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Date deposited: 02 Jan 2012 11:02
Last modified: 19 Jul 2019 22:06

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