Sigmoid-based incorrect opinion prevention algorithm on multi-opinion sharing model

Fumito Uwano, Eiki Kitajima, Keiki Takadama

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

To improve the accuracy to prevent from sharing incorrect opinion, this paper proposes a method which can share correct opinions based on majority decision for multi-opinion, named Gradient Descent Weight Tuning (GDWT). In the experiment, this paper compares GDWT with AAT and Self-information Weight Tuning (SWT) which weights the opinion from the agent has clear information from the environment to share correct opinion based on majority decision from environment-to-agent information as the previous methods. From the result of the experiment to investigate the proposed methods on some complex networks with multi-opinion, this paper reveals that (1) though the previous method performs worse in order from less kinds of opinions, the proposed methods performs well (SWT: 0.8, GDWT: 0.9 accuracy); (2) GDWT performs the best without incorrect opinions; and (3) It is clear that GDWT is sensitive for the received incorrect opinions and the own parameters, especially target opinion formation rate, as comparing with SWT. These issues are discussed for future works.

Original languageEnglish
Pages (from-to)B-KB2_1-B-KB2_12
JournalTransactions of the Japanese Society for Artificial Intelligence
Volume36
Issue number6
DOIs
Publication statusPublished - 2021

Keywords

  • Multi-agent network
  • Multi-opinion
  • Opinion sharing model
  • Sigmoid function

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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