HARM: A Hybrid Rule-Based Agent Reputation Model Based on Temporal Defeasible Logic

Kravari, Kalliopi; Bassiliades, Nick

doi:10.1007/978-3-642-32689-9_15

Cited by 7 publications

(15 citation statements)

References 20 publications

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“…In some practical reputation evaluation systems and research works, trust scores are used as basic elements in the computation of reputation [9,[56][57][58][59]. However, direct trust and total trust are not distinguished.…”

Section: Variables In the Modelsupporting

confidence: 41%

Closed-Loop Feedback Computation Model of Dynamical Reputation Based on the Local Trust Evaluation in Business-to-Consumer E-Commerce

Tian

Han²,

Liu³

2016

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Trust and reputation are important factors that influence the success of both traditional transactions in physical social networks and modern e-commerce in virtual Internet environments. It is difficult to define the concept of trust and quantify it because trust has both subjective and objective characteristics at the same time. A well-reported issue with reputation management system in business-to-consumer (BtoC) e-commerce is the "all good reputation" problem. In order to deal with the confusion, a new computational model of reputation is proposed in this paper. The ratings of each customer are set as basic trust score events. In addition, the time series of massive ratings are aggregated to formulate the sellers' local temporal trust scores by Beta distribution. A logical model of trust and reputation is established based on the analysis of the dynamical relationship between trust and reputation. As for single goods with repeat transactions, an iterative mathematical model of trust and reputation is established with a closed-loop feedback mechanism. Numerical experiments on repeated transactions recorded over a period of 24 months are performed. The experimental results show that the proposed method plays guiding roles for both theoretical research into trust and reputation and the practical design of reputation systems in BtoC e-commerce.

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Section: Variables In the Modelsupporting

confidence: 41%

Closed-Loop Feedback Computation Model of Dynamical Reputation Based on the Local Trust Evaluation in Business-to-Consumer E-Commerce

Tian

Han²,

Liu³

2016

Information

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“…Moreover, in case of witness reports, self interested agents could be unwilling or unable to sacrifice their resources in order to provide reports. As a result, this approach alone could not guarantee a reliable estimation [3]. In this article we have presented a hybrid reputation model and compared its results with other information sources.…”

Section: Introductionsupporting

confidence: 40%

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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“…Furthermore, in order to obtain an accurate result for performance comparisons between trust models, each one will be employed by a large but equal number of consumer agents. [27] and NONE (no trust mechanism, randomly selected providers). We used HARM although it is a centralized approach since it is a rule-based model using temporal defeasible reasoning.…”

Section: Measuring Estimation Confidencementioning

confidence: 41%

DISARM: A social distributed agent reputation model based on defeasible logic

Kravari

Bassiliades

2016

Journal of Systems and Software

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Intelligent Agents act in open and thus risky environments, hence making the appropriate decision about who to trust in order to interact with, could be a challenging process.As intelligent agents are gradually enriched with Semantic Web technology, acting on behalf of their users with limited or no human intervention, their ability to perform assigned tasks is scrutinized. Hence, trust and reputation models, based on interaction trust or witness reputation, have been proposed, yet they often presuppose the use of a centralized authority. Although such mechanisms are more popular, they are usually faced with skepticism, since users may question the trustworthiness and the robustness of a central authority. Distributed models, on the other hand, are more complex but they provide personalized estimations based on each agent's interests and preferences. To this end, this article proposes DISARM, a novel distributed reputation model. DISARM deals MASs as social networks, enabling agents to establish and maintain relationships, limiting the disadvantages of the common distributed approaches. Additionally, it is based on defeasible logic, modeling the way intelligent agents, like humans, draw reasonable conclusions from incomplete and possibly conflicting (thus inconclusive) information. Finally, we provide an evaluation that illustrates the usability of the proposed model.

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HARM: A Hybrid Rule-Based Agent Reputation Model Based on Temporal Defeasible Logic

Cited by 7 publications

References 20 publications

Closed-Loop Feedback Computation Model of Dynamical Reputation Based on the Local Trust Evaluation in Business-to-Consumer E-Commerce

Closed-Loop Feedback Computation Model of Dynamical Reputation Based on the Local Trust Evaluation in Business-to-Consumer E-Commerce

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

DISARM: A social distributed agent reputation model based on defeasible logic

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