Evaluation of trust in an ecommerce multi-agent system using fuzzy reasoning

Hnativ, Andriy; Ludwig, Simone A.

doi:10.1109/fuzzy.2009.5277344

Cited by 7 publications

(2 citation statements)

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“…We call this approach the graph analogy-based approach. There are also some approaches which use classical mathematical algorithms to infer trust, such as fuzzy logic [7] and Bayesian theory [8].…”

Section: Introductionmentioning

confidence: 99%

FlowTrust: trust inference with network flows

Wang

2011

Front. Comput. Sci. China

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Web-based social networking is increasingly gaining popularity due to the rapid development of computer networking technologies. However, social networking applications still cannot obtain a wider acceptance by many users due to some unresolved issues, such as trust, security, and privacy. In social networks, trust is mainly studied whether a remote user behaves as expected by an interested user via other users, who are respectively named trustee, trustor, and recommenders. A trust graph consists of a trustor, a trustee, some recommenders, and the trust relationships between them. In this paper, we propose a novel FlowTrust approach to model a trust graph with network flows, and evaluate the maximum amount of trust that can flow through a trust graph using network flow theory. FlowTrust supports multi-dimensional trust. We use trust value and confidence level as two trust factors. We deduce four trust metrics from these two trust factors, which are maximum flow of trust value, maximum flow of confidence level, minimum cost of uncertainty with maximum flow of trust, and minimum cost of mistrust with maximum flow of confidence. We also propose three FlowTrust algorithms to normalize these four trust metrics. We compare our proposed FlowTrust approach with the existing RelTrust and CircuitTrust approaches. We show that all three approaches are comparable in terms of the inferred trust values. Therefore, FlowTrust is the best of the three since it also supports multi-dimensional trust.

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Section: Introductionmentioning

confidence: 99%

FlowTrust: trust inference with network flows

Wang

2011

Front. Comput. Sci. China

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“…This means that consumers may buy goods from companies which they have not interacted with before, and whom they do not know. Therefore, it leads to uncertainty about the product provided by provider and this platform needs to incorporate issues such as trust and help make the transaction more secure and reduce the uncertainty [9].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Artificial Reputation Model Involving Interaction Trust, Witness Information and the Trust Model to Calculate the Trust Value of Service Providers

Ransi

Kobti

2014

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Agent interaction in a community, such as the online buyer-seller scenario, is often uncertain, as when an agent comes in contact with other agents they initially know nothing about each other. Currently, many reputation models are developed that help service consumers select better service providers. Reputation models also help agents to make a decision on who they should trust and transact with in the future. These reputation models are either built on interaction trust that involves direct experience as a source of information or they are built upon witness information also known as word-of-mouth that involves the reports provided by others. Neither the interaction trust nor the witness information models alone succeed in such uncertain interactions. In this paper we propose a hybrid reputation model involving both interaction trust and witness information to address the shortcomings of existing reputation models when taken separately. A sample simulation is built to setup buyer-seller services and uncertain interactions. Experiments reveal that the hybrid approach leads to better selection of trustworthy agents where consumers select more reputable service providers, eventually helping consumers obtain more gains. Furthermore, the trust model developed is used in calculating trust values of service providers.

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