Survey on Computational Trust and Reputation Models

2019 ◽  
Vol 51 (5) ◽  
pp. 1-40 ◽  
Author(s):  
Diego De Siqueira Braga ◽  
Marco Niemann ◽  
Bernd Hellingrath ◽  
Fernando Buarque De Lima Neto
Keyword(s):  
Computational Trust ◽  
Trust And Reputation

Review on Computational Trust and Reputation Models

2005 ◽  
Vol 24 (1) ◽  
pp. 33-60 ◽  
Author(s):  
Jordi Sabater ◽  
Carles Sierra
Keyword(s):  
Computational Trust ◽  
Trust And Reputation

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.

A Policy-Oriented Reputation BDI Model

2010 ◽  
Vol 34-35 ◽  
pp. 707-711 ◽  
Author(s):  
Jun Hu ◽  
Yang Yu
Keyword(s):  
Knowledge Representation ◽  
Dynamic Environment ◽  
Knowledge Representation And Reasoning ◽  
Agent Model ◽  
Computational Trust ◽  
Agent Systems ◽  
Defeasible Logic ◽  
Trust And Reputation ◽  
Key Technologies ◽  
Policy Guidance

Computational trust and reputation models have been recognized as one of the key technologies required to design and implement agent systems. These models manage and aggregate the information needed by agents to efficiently perform partner selection in uncertain situations. In this paper, by taking advantage of the non-monotonic knowledge representation and reasoning mechanisms of defeasible logic, a reputation-Oriented Agent model is proposed, which is capable of accepting policy guidance, the real-time rule modifications, and handling the run-time rule conflicts. This agent is both autonomous and controllable, and is able to cooperate with other Agents via contracts in an open and dynamic environment.

Neural Trust Model

2017 ◽  
pp. 296-316
Author(s):  
Gehao Lu ◽  
Joan Lu
Keyword(s):  
Theoretical Foundation ◽  
Dynamic Structure ◽  
Trust Model ◽  
Static Structure ◽  
Specific Component ◽  
Learning Capability ◽  
Computational Trust ◽  
Trust And Reputation ◽  
Proposed Model ◽  
Definition Of

The problems found in the existing models push the researcher to look for a better solution for computational trust and computational reputation. According the problem exposed earlier, the newly proposed model should be a systematic model which supports both trust and reputation. The model should also take the learning capability for agents into consideration because agents cannot quickly adapt to the changes without learning. The model also needs to have the ability to make decisions according to its recognition of trust. Before actually building the model, it is necessary to analyze the concept of trust. Usually when people say trust they mean human trust, however, in this research trust refers to computational trust. How human trust is different from computational trust is a very interesting question. The answers to the question helped the researcher recover many features of computational trust and built a solid theoretical foundation for the proposed model. The definitions of trust in different disciplines such as economy, sociology and psychology will be compared. A possible definition of computational trust will be made and such trust from several different perspectives will be analyzed. The description of the model is important. As a whole, it is represented as a framework that defines components and component relationships. As the concrete components, the purposes and responsibilities of the specific component are explained. This is to illustrate the static structure of the model. The dynamic structure of the model is described as the process of executing the model.

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