Research on Trust Model Based on Behavior and Trusted Computing

Gu, Yue Sheng; Wang, Yan Rong

doi:10.4028/www.scientific.net/amr.108-111.1259

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Disrepute is computed based on the negative opinions of other agents about a specific agent, while the reputation of each agent is based on the positive opinions of other agents about a specific agent. In fact, requester agents can avoid the risk of purchasing and maximize their expected value of goods by dynamically maintaining and considering both sets of reputation and disrepute of a provider agent (Brusilovsky et al, 2003;Gu et al, 2010). Accordingly, an agent, who has to select the most promising agent, should consider the value of disrepute of that agent in a multi-agent environment, along with its reputation value.…”

Section: Imds 1167mentioning

confidence: 99%

A reputation-oriented trust model for multi-agent environments

Majd

Balakrishnan

2016

Industr Mngmnt & Data Systems

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Purpose – The purpose of this paper is to enhance trust in multi-agent systems by presenting a new computational model, named reputation-distribute-conflict (R-D-C), to select the most trustworthy provider agent based on computing reputation, disrepute, and conflict of each provider agent. Design/methodology/approach – R-D-C propose based on three vital components for evaluating trustworthiness of providers as reputation, disrepute, and conflict, where disrepute is a component almost all trust models ignored. The R-D-C model presents a computational method for evaluating to select the most trustworthy provider agent. In order to evaluate the R-D-C model, the experimentation was carried out in two stages, by designing a simulated multi-agent environment. First, the accuracy of the R-D-C model in computing R-D-C was investigated. Second, the performance of the model was compared with other existing trust models. Moreover, comparison of the performance of the R-D-C model with other models demonstrates that the R-D-C model performs significantly better than the other models. Therefore, the R-D-C model is capable of evaluating the trustworthiness of agents more accurately and it can select the most trustworthy provider better than the other models. Findings – The results show that the R-D-C model works well in different multi-agent environments, even when the number of untrustworthy providers is higher than that of the trustworthy ones. Originality/value – The R-D-C model is useful for researchers to enhance the safety of online transactions in multi-agent environments, especially if the researchers explore more components; in fact the R-D-C model is capable of adding these new components and selects the most trustworthy provider agent.

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Section: Imds 1167mentioning

confidence: 99%

A reputation-oriented trust model for multi-agent environments

Majd

Balakrishnan

2016

Industr Mngmnt & Data Systems

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“…In MAS, agents are autonomous and behave by selfinterested reasons, so they could be dishonest and incapable and be given disrepute [22,23]. Dissatisfying decisions of agent interactions constitute the disrepute [24].…”

Section: Introductionmentioning

confidence: 99%

A Multiagent Cooperation Model Based on Trust Rating in Dynamic Infinite Interaction Environment

Fan

2018

Mathematical Problems in Engineering

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To improve the liveness of agents and enhance trust and collaboration in multiagent system, a new cooperation model based on trust rating in dynamic infinite interaction environment (TR-DII) is proposed. TR-DII model is used to control agent’s autonomy and selfishness and to make agent do the rational decision. TR-DII model is based on two important components. One is dynamic repeated interaction structure, and the other is trust rating. The dynamic repeated interaction structure is formed with multistage inviting and evaluating actions. It transforms agents’ interactions into an infinity task allocation environment, where controlled and renewable cycle is a component most agent models ignored. Additionally, it influences the expectations and behaviors of agents which may not appear in one-shot time but may appear in long-time cooperation. Moreover, with rewards and punishments mechanism (RPM), the trust rating (TR) is proposed to control agent blindness in selection phase. However, RPM is the factor that directly influences decisions, not the reputation as other models have suggested. Meanwhile, TR could monitor agent’s statuses in which they could be trustworthy or untrustworthy. Also, it refines agent’s disrepute in a new way which is ignored by the others. Finally, grids puzzle experiment has been used to test TR-DII model and other five models are used as comparisons. The results show that TR-DII model can effectively adjust the trust level between agents and makes the solvers be more trustworthy and do choices that are more rational. Moreover, through interaction result feedback, TR-DII model could adjust the income function, to control cooperation reputation, and could achieve a closed-loop control.

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SocialST: Social Liveness and Trust Enhancement Based Social Recommendation

Lin

Shi

et al. 2019

2019 IEEE International Conference on Web Services (ICWS)

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Research on Trust Model Based on Behavior and Trusted Computing

Cited by 3 publications

References 2 publications

A reputation-oriented trust model for multi-agent environments

A reputation-oriented trust model for multi-agent environments

A Multiagent Cooperation Model Based on Trust Rating in Dynamic Infinite Interaction Environment

SocialST: Social Liveness and Trust Enhancement Based Social Recommendation

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