Preventing Incorrect Opinion Sharing with Weighted Relationship Among Agents

Rei Saito, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Takadama Keiki

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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This paper aims at investigating how correct or incorrect opinions are shared among the agents in the weighted network where the relationship among the agent (as nodes of its network) is different each other, and exploring how the agents can be promoted to share only correct opinions by preventing to acquire the incorrect opinions in the weighted network. For this purpose, this paper focuses on Autonomous Adaptive Tuning algorithm (AAT) which can improve an accuracy of correct opinion shared among agents in the various network, and improves it to address the situation which is close in the real world, i.e., the relationship among agents is different each other. This is because the original AAT does not consider such a different relationship among the agents. Through the intensive empirical experiments, the following implications have been revealed: (1) the accuracy of the correct opinion sharing with the improved AAT is higher than that with the original AAT in the weighted network; (2) the agents in the improved AAT can prevent to acquire incorrect opinion sharing in the weighted network, while those in the original AAT are hard to prevent in the same network.
Original languageEnglish
Title of host publicationHuman Interface and the Management of Information: Applications and Services
Subtitle of host publication18th International Conference, HCI International 2016 Toronto, Canada, July 17-22, 2016. Proceedings, Part II
Number of pages13
ISBN (Electronic)9783319403977
ISBN (Print)9783319403960
Publication statusPublished - 21 Jun 2016

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


  • Multi agent system
  • Community computing
  • Learning communities


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