A Review on Computational Trust Models for Multi-agent Systems

Lu, Gehao; Lu, Joan; Yao, Shaowen; Yip, Jim

doi:10.2174/1874947x00902020018

Cited by 15 publications

(11 citation statements)

References 9 publications

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“…32 Others are more general such as in other works. 5,7,8,27,[33][34][35][36][37] A recent review of reputation system evaluation approaches has been presented in the work of Koutrouli and Tsalgatidou. 28 This section highlights a selection of notable related models with a particular emphasis on their applicability in open MASs.…”

Section: Related Workmentioning

confidence: 99%

A hybrid trust model using reinforcement learning and fuzzy logic

Aref

Tran

2017

Computational Intelligence

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Multiagent systems (MASs) are increasingly popular for modeling distributed environments that are highly complex and dynamic, such as e‐commerce, smart buildings, and smart grids. Typically, agents assumed to be goal driven with limited abilities, which restrains them to working with other agents for accomplishing complex tasks. Trust is considered significant in MASs to make interactions effectively, especially when agents cannot assure that potential partners share the same core beliefs about the system or make accurate statements regarding their competencies and abilities. Due to the imprecise and dynamic nature of trust in MASs, we propose a hybrid trust model that uses fuzzy logic and Q‐learning for trust modeling. as an improvement over Q‐learning‐based trust evaluation. Q‐learning is used to estimate trust on the long term, fuzzy inferences are used to aggregate different trust factors, and suspension is used as a short‐term response to dynamic changes. The performance of the proposed model is evaluated using simulation. Simulation results indicate that the proposed model can help agents select trustworthy partners to interact with. It has a better performance compared to some of the popular trust models in the presence of misbehaving interaction partners.

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Section: Related Workmentioning

confidence: 99%

A hybrid trust model using reinforcement learning and fuzzy logic

Aref

Tran

2017

Computational Intelligence

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“…As mentioned, delegation of an action relates to trust of a delegated agent who offers to execute that action. It forms a foundation of future cooperative large-scale computer systems [101]. The notion of delegation is meaningful in the service workflow context, where one service can be hierarchically composed of multiple sub-services and these services might be delegated to other service providers (delegatees).…”

Section: G Service Delegation (T9) and Trust Transitivity (T10) Propmentioning

confidence: 99%

A Survey of Trust in Workflows and Relevant Contexts

Viriyasitavat

Martin

2011

IEEE Commun. Surv. Tutorials

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With the fast-growing Internet technology, several service-based interactions are prevalent and appear in several forms such as e-commerce, content provider, Virtual Organizations, Peer-to-Peer, Web Service, Grids, Cloud Computing, and individual interactions. This demands for an effective mechanism to establish trust among participants in a highlevel abstract way, capturing relevant factors ranging on Service Level Agreement, security policies, requirements, regulations, constraints, Quality of Service, reputation, and recommendation. Trust is platform-independent and flexible to be seamlessly integrated into heterogeneous domains and interoperate with different security solutions in distributed environments. Establishing trust in a service workflow leads to the willingness of services to participate. Coordinating service workflows without trust consideration may pose higher risks, possibly results in poor performance, additional vulnerabilities, or failures. Although trust in service workflows and relevant contexts has been studied for a past decade, the standard development is still immature. Nowadays, trust approaches to service workflows comprise a large area of research where one can hardly classify into a comprehensive survey. This survey examines and explores the role of trust in service workflows and their contexts from a wide variety of literatures. Various mechanisms, architecture, techniques, standards, and frameworks are explained along the way with discussions. Working trust definition and classification are newly provided and supported with examples.

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“…Therefore, heterogeneous applications may consist of a number of agents extending the complexity of such interactions between them. This complexity, and the comprehensive nature of VCEs, represents a challenge how one agent discovers and accepts services from unknown agents, while considering unknown agent's interaction history as a source to build a reputation [2].…”

Section: Introductionmentioning

confidence: 99%

A Graph-Based Approach to Address Trust and Reputation in Ubiquitous Networks

Viduto

Townend

et al. 2013

2013 IEEE Seventh International Symposium on Service-Oriented System Engineering

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Abstract-The increasing popularity of virtual computing environments such as Cloud and Grid computing is helping to drive the realization of ubiquitous and pervasive computing. However, as computing becomes more entrenched in everyday life, the concepts of trust and risk become increasingly important. In this paper, we propose a new graph-based theoretical approach to address trust and reputation in complex ubiquitous networks. We formulate trust as a function of quality of a task and time required to authenticate agent-toagent relationship based on the Zero-Common Knowledge (ZCK) authentication scheme. This initial representation applies a graph theory concept, accompanied by a mathematical formulation of trust metrics. The approach we propose increases awareness and trustworthiness to agents based on the values estimated for each requested task; we conclude by stating our plans for future work in this area.

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A Review on Computational Trust Models for Multi-agent Systems

Cited by 15 publications

References 9 publications

A hybrid trust model using reinforcement learning and fuzzy logic

A hybrid trust model using reinforcement learning and fuzzy logic

A Survey of Trust in Workflows and Relevant Contexts

A Graph-Based Approach to Address Trust and Reputation in Ubiquitous Networks

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