Recommending Services in a Trust-Based Decentralized User Modeling System

Nusrat, Sabrina; Vassileva, Julita

doi:10.1007/978-3-642-28509-7_22

Cited by 12 publications

(10 citation statements)

References 18 publications

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“…The solutions to managing quality or non-functional requirements, such as trust, reputation, and privacy, for agent systems were identified in a systematic literature review [10]. Trust and reputation management models, such as REGRET [11], A Fuzzy Reputation Agent System (AFRAS) [12], FIRE+ [13], Nusrat [14], and ScubAA [15], have previously been proposed. Table 1 presents the mapping of agent quality management literature with the quality properties.…”

Section: Quality Management In Agent Systemsmentioning

confidence: 99%

“…For managing trust and reputation, the FIRE+ model [13] incorporated information from multiple sources to manage trust and reputation and to comprehensively assess agent performance in open systems. Nusrat and Vassileva [14] proposed multi-faceted trust values to be implemented in decentralized user modeling to manage trust and reputation. ScubAA [15] was presented as an agent trust management framework for open systems.…”

Section: Quality Management In Agent Systemsmentioning

confidence: 99%

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Improving Agent Quality in Dynamic Smart Cities by Implementing an Agent Quality Management Framework

2019

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It is critical for quality requirements, such as trust, privacy, and confidentiality, to be fulfilled during the execution of smart city applications. In this study, smart city applications were modeled as agent systems composed of many agents, each with its own privacy and confidentiality properties. Violations of those properties may occur during execution due to the dynamic of agent behavior, decision-making capabilities, and social activities. In this research, a framework called Agent Quality Management was proposed and implemented to manage agent quality in agent systems. This paper demonstrates the effectiveness of the approach by applying it toward a smart city application called a crowdsourced navigation system to verify and assess agent data confidentiality. The AnyLogic Agent-Based Modeling tool was used to model and conduct the experiments. The experiments showed that the framework helped to improve the detection of agent quality violations in a dynamic smart city application. The results can be further analyzed using advanced data analytic approach to reduce future violations and improve data confidentiality in a smart city environment.

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Section: Quality Management In Agent Systemsmentioning

confidence: 99%

Section: Quality Management In Agent Systemsmentioning

confidence: 99%

Improving Agent Quality in Dynamic Smart Cities by Implementing an Agent Quality Management Framework

2019

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“…Moreover, a weighting scheme is emp loyed to prio rit ize different QoS metrics. Authors of [21] introduce a method for evaluating trust and reputation of a service provider in a decentralized user modeling system. The proposed method takes into account two metrics: a number of measures with regard to the QoS of service provider, and the degree o f trust over each of these measures in the case where they are provided by other agents that show how much the user trust the referrers values in that context.…”

Section: Trust Based Service Selectionmentioning

confidence: 99%

A Rough Sets-based Agent Trust Management Framework

Abedinzadeh

Sadaoui²

2013

IJISA

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Abstract-In a virtual society, which consists of several autonomous agents, trust helps agents to deal with the openness of the system by identify ing the best agents capable of performing a specific task, or achieving a special goal. In this paper, we introduce ROSTAM, a new approach for agent trust management based on the theory of Rough Sets. ROSTAM is a generic trust management framework that can be applied to any types of mu lti agent systems. However, the features of the application do main must be provided to ROSTAM. These features form the trust attributes. By collecting the values for these attributes, ROSTAM is able to generate a set of trust rules by employing the theory of Rough Sets. ROSTAM then uses the trust rules to extract the set of the most trusted agents and forwards the user's request to those agents only. After getting the results, the user must rate the interaction with each trusted agent. The rating values are subsequently utilized for updating the trust rules. We applied ROSTAM to the domain o f cross -language Web search. The resulting Web search system reco mmends to the user the set of the most trusted pairs of translator and search engine in terms of the pairs that return the results with the highest precision of retrieval.

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“…A new agent in the environment may cause a dilemma, especially for systems which base trust solely on reputation. Trust models should allocate an arbitrary level of trust, generally a median value to this new agent [7], however, the provision of proper initial trust or reputation value to each newcomer should be done carefully as it will have a direct effect on the selection of profitable [2]. One possible solution is to incorporate a reputation brokering mechanism in which each user can customize its strategies according to the risk implied by the reputation values of its potential counterparts [11].…”

Section: Standard Componentsmentioning

confidence: 99%

“…In contrast, the user models are maintained locally by the agents in a distributed architecture. It is not necessary to reveal personal information to a central server, and agents also communicate with one another to collect information or find resources and experts in order to pursue their users' [7].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Trust Models for Multi-Agent Systems

Balakrishnan¹

2013

LNSE

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Multi-agent systems can break interactions in distributed and heterogeneous environments, therefore, trust models play a critical role in determining how interactions take place between multiple agents. This paper compared and analyzed some recent trust models based on five important components: architecture, dimension, initial trust, reputation, and risk. Overall results indicate risk and initial trust to be the weakest components, whereas dimension, reputation and architecture are the strong components of the existing trust models. This analysis helps to introduce the standard components of trust and reputation models for e-commerce multi-agent systems, and to identify the weakness and strengths of the models based on these standard components

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Recommending Services in a Trust-Based Decentralized User Modeling System

Cited by 12 publications

References 18 publications

Improving Agent Quality in Dynamic Smart Cities by Implementing an Agent Quality Management Framework

Improving Agent Quality in Dynamic Smart Cities by Implementing an Agent Quality Management Framework

A Rough Sets-based Agent Trust Management Framework

A Comparative Analysis of Trust Models for Multi-Agent Systems

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