scholarly journals A Trust and Reputation Model as Adaptive Mechanism for Multi-Agent Systems

2009 ◽  
Vol 13 (42) ◽  
Author(s):  
Alberto Caballero ◽  
Teresa García-Valverde ◽  
Juan A. Botia ◽  
Antonio Gomez-Skarmeta
Keyword(s):  
Adaptive Mechanism ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Trust And Reputation ◽  
Multi Agent ◽  
Reputation Model

scholarly journals An integrated trust and reputation model for open multi-agent systems

2006 ◽  
Vol 13 (2) ◽  
pp. 119-154 ◽  
Author(s):  
Trung Dong Huynh ◽  
Nicholas R. Jennings ◽  
Nigel R. Shadbolt
Keyword(s):  
Multi Agent Systems ◽  
Agent Systems ◽  
Trust And Reputation ◽  
Multi Agent ◽  
Reputation Model

Introduction to the Investigating in Neural Trust and Multi Agent Systems

2017 ◽  
pp. 269-273 ◽  
Author(s):  
Gehao Lu ◽  
Joan Lu
Keyword(s):  
Trust Model ◽  
Self Organizing Map ◽  
Multi Agent Systems ◽  
Test Bed ◽  
Computational Trust ◽  
Agent Systems ◽  
Reputation Mechanism ◽  
Trust And Reputation ◽  
Multi Agent ◽  
Propagation Methods

Introducing trust and reputation into multi-agent systems can significantly improve the quality and efficiency of the systems. The computational trust and reputation also creates an environment of survival of the fittest to help agents recognize and eliminate malevolent agents in the virtual society. The research redefines the computational trust and analyzes its features from different aspects. A systematic model called Neural Trust Model for Multi-agent Systems is proposed to support trust learning, trust estimating, reputation generation, and reputation propagation. In this model, the research innovates the traditional Self Organizing Map (SOM) and creates a SOM based Trust Learning (STL) algorithm and SOM based Trust Estimation (STE) algorithm. The STL algorithm solves the problem of learning trust from agents' past interactions and the STE solve the problem of estimating the trustworthiness with the help of the previous patterns. The research also proposes a multi-agent reputation mechanism for generating and propagating the reputations. The mechanism exploits the patterns learned from STL algorithm and generates the reputation of the specific agent. Three propagation methods are also designed as part of the mechanism to guide path selection of the reputation. For evaluation, the research designs and implements a test bed to evaluate the model in a simulated electronic commerce scenario. The proposed model is compared with a traditional arithmetic based trust model and it is also compared to itself in situations where there is no reputation mechanism. The results state that the model can significantly improve the quality and efficacy of the test bed based scenario. Some design considerations and rationale behind the algorithms are also discussed based on the results.

scholarly journals COBRA: Context-Aware Bernoulli Neural Networks for Reputation Assessment

2020 ◽  
Vol 34 (05) ◽  
pp. 7317-7324
Author(s):  
Leonit Zeynalvand ◽  
Tie Luo ◽  
Jie Zhang
Keyword(s):  
Large Scale ◽  
Service Providers ◽  
Reputation Management ◽  
The Internet ◽  
Context Aware ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Trust And Reputation ◽  
Multi Agent ◽  
The Internet Of Things

Trust and reputation management (TRM) plays an increasingly important role in large-scale online environments such as multi-agent systems (MAS) and the Internet of Things (IoT). One main objective of TRM is to achieve accurate trust assessment of entities such as agents or IoT service providers. However, this encounters an accuracy-privacy dilemma as we identify in this paper, and we propose a framework called Context-aware Bernoulli Neural Network based

Design of an Intelligent System to Support Tutors in Learning Communities Using Multi-Agent Systems and Fuzzy Logic

2015 ◽  
Vol 10 (8) ◽  
pp. 845 ◽  
Author(s):  
Youness Chaabi ◽  
R. Messoussi ◽  
V. Hilaire ◽  
Y. Ruichek ◽  
K. Lekdioui ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Learning Communities ◽  
Intelligent System ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent
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