An Abstract Framework for Introducing Computational Trust Models in JADE-Based Multi-Agent Systems

Mifrah, Youssef; En‐Nouaary, Abdeslam; Dahchour, Mohamed

doi:10.1007/978-981-287-990-5_41

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…We have followed those practices while designing and implementing our testbed .We are unable to discuss the implementation details of each component in this paper, but we will give an overview of the design structure and the role of each part. The implementation code of the testbed is available at our public repository [30] that contains also some of our previous published works [31], [32]. There are six distinct components that compose the testbed: Students, Operations, Attack strategies, Tasks, Features and Trust models, Fig.5 shows the relationships between those components.…”

Section: Testbed Implementationmentioning

confidence: 99%

Towards a Framework for the Analysis and Evaluation of Computational Trust Models in Multi-agent Systems

Mifrah¹,

En‐Nouaary²,

Dahchour³

2017

JSW

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Abstract:In open dynamic multi-agent systems, trust is commonly considered as a critical concept to be handled and managed. Computational trust models are a kinds of formal models that have been proposed to manage trust in such situation. These models present a new form of distributed intelligence in virtual societies and collective intelligence. However, the diversity of those models makes user confused about which one to choose. Different testbeds have been established to evaluate trust and reputation models and verify their robustness and efficiency. However, a lack of flexibility to handle scenarios related to multi-context trust models arise with those testbeds. We present in this paper a framework for evaluating computational trust models that provides to users more flexibility while comparing trust models in open systems and shows analysis results in chart diagrams. The ultimate objective is to evaluate and classify available computational trust models.

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Section: Testbed Implementationmentioning

confidence: 99%