Correcting wrongly determined opinions of agents in opinion sharing model

This paper aims at achieving a stable high accuracy of opinion sharing in a distributed network with the agents which have initial opinions. Specifically, the network is composed of multi-agents, and most agents form their opinions according to the neighbors opinions which may be incorrect while a few agents only can receive outside information which is expected to be correct but may be incorrect with noise. To order for the agents to form the correct opinions, we employ Autonomous Adaptive Tuning algorithm (AAT) which can improve the rate of correct opinion shared among the agents where incorrect opinions are filtered out during the opinion sharing process. However, AAT is hard to promote agents to form the correct opinions when all agents have their initial opinions. To tackle this problem, we proposed Autonomous Adaptive Tuning Dynamic (AATD) for the network where initial opinions of all agents are unknown. The intensive experiments have revealed, the following implications: (1) the accuracy rate of the agents with AATD is stably 70%–80% regardless initial opinion state in small network, while the accuracy rate with AAT varies from 0% to 100% depending on the state of the initial opinion; and (2) AATD is robust to different complex network topology in comparison with AAT.