Acting as a Trustee for Internet of Agents in the Absence of Explicit Feedback

Aref, Abdullah; Tran, Thomas

doi:10.1007/978-3-319-59041-7_1

Cited by 4 publications

(8 citation statements)

References 19 publications

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“…ATeIF is a multi-criteria trust establishment model that incorporates indirect feedback from trustors to help trustees adjust their behaviors toward individual trustors. 7 In this system, tasks that can be performed or requested contain various criteria which each are assigned weight values by the model to represent the importance of the criterion for each trustor. Each criterion corresponds to a specific aspect of the task, such as the time to completion or the quality of the product/service.…”

Section: Decentralized Trust Establishment Modelsmentioning

confidence: 99%

Cluster‐based improvement rates for trust establishment models in single or distributed multi‐agent systems

Templeton

Tran

2022

Computational Intelligence

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Intelligent agents within open and dynamic multi-agent systems are becoming increasingly capable in their decision-making abilities and rely upon the notion of trustworthiness to determine which agents to interact with. To improve the overall performance of How to cite this article: Templeton J, Tran T. Cluster-based improvement rates for trust establishment models in single or distributed multi-agent systems.

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Section: Decentralized Trust Establishment Modelsmentioning

confidence: 99%

Cluster‐based improvement rates for trust establishment models in single or distributed multi‐agent systems

Templeton

Tran

2022

Computational Intelligence

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“…ITE uses trustor retention, Q-Learning, indirect and direct feedback, and dynamic hyperparameters to achieve high trust values while minimizing the UG being provided. Many of ITE's ideas originate from the Reinforcement Learning Based Trust Establishment (RLTE) model [4] and the Acting as a Trustee Using Implicit Feedback (ATeIF) model [5].…”

Section: Background Informationmentioning

confidence: 99%

Improved Trust Establishment Performance with Transaction-based Preprocessing

Templeton

Tran

2021

Proceedings of the Canadian Conference on Artificial Intelligence

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In Multi-Agent Systems, trustees can utilize trust establishment models to help improve their trust with trustors in the environment. This improved trust helps to improve the quality of interactions in the environment and increases the viability of a trustee as an interaction partner. To help current and future trust establishment models become more modular to adapt to changes brought by future research and to introduce a method to allow for improved performance, a generalized trust establishment model architecture is presented. The proposed Generalized Trust Establishment Model is a simplistic model which illustrates how the generalized architecture can be used to design a competent trust establishment model. This model uses the architecture's newly proposed transaction-level preprocessing module to achieve strong simulation results. This preprocessing module allows a model to fine-tune the amount of resources that will be given to trustors before the transactions occur to help more accurately meet the needs of trustors. Using the preprocessing module, the proposed model better meets a trustor's needs and achieves a higher average trust in the environment faster than when the preprocessing is not performed. This exhibits that preprocessing is an important technique that can be used by any trust establishment model to improve the model's performance.

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“…Trustees dynamically tune their performance using reinforcement learning depending on the retention of trustors. Similar to RLTE, other decentralized models for trust establishment presented in (Aref and Tran, 2016;2015a;2017a) use retention of trustors. Those models typically use different computation engines; (Aref and Tran, 2015a) uses fuzzy logic while (Aref and Tran, 2016) relies on a set of custom equations to help trustees dynamically predict trustors' behaviour.…”

Section: Related Workmentioning

confidence: 99%

“…The multi-criteria trust establishment model presented in (Aref and Tran, 2017a) attempts to predict both the proper value per criteria and its importance. The model tunes the performance of trustees with respect to each criterion based on the retention of trustors as well as the relative weight of the particular criterion.…”

Section: Related Workmentioning

confidence: 99%

“…is the membership function of the aggregated fuzzy set IMP. Unlike RLTE (Aref and Tran, 2015b), ATeIF (Aref and Tran, 2017a) and ITE (Aref and Tran, 2020a), MCFTE does not depend on implicit feedback from trustors to tune up the performance of trustee(s). Arguably, a trustee can depend on the level of satisfaction reported by each trustor per criterion with its importance, combined with the utility gain proposed by the trustee for the same criterion to determine the necessary performance enhancement, if each trustor is will to accurately report the level of satisfaction per criterion.…”

Section: Defuzzificationmentioning

confidence: 99%

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Multi-Criteria Trust Establishment for Multi-Agent Systems Based on Fuzzy Logic

Aref

Omar

2020

Journal of Computer Science

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Multi-Agent Systems (MASs) are increasingly accepted for modeling virtual complex distributed systems, such as virtual societies and smart grids, due to agents' autonomy, flexibility and pro-activity. As autonomous, goal-driven agents can mislead others intentionally or accidentally by inaccurately reporting their competencies and abilities, the use of trust modeling deemed essential for successful interactions in MASs. The concept of trust is complex, multidimensional and includes more than just evaluating how honest interaction partners are. This study describes an explicit, multi-criteria, trust establishment model based on fuzzy logic to guide trustees in MASs to improve their level of trust as perceived by the trustor by tuning up their behaviors to attract more interactions with potential partners. When trustors are willing to provide feedback for interactions in the form of a single satisfaction value per multi-criterion interaction, the model attempts to predict the necessary improvement per criterion. We evaluated the performance of the proposed model using simulation. The results indicate that the model can help trustees achieve higher trust levels and get better chances to be selected as partners for interactions in MASs when trustors select trustees based on trust.

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Acting as a Trustee for Internet of Agents in the Absence of Explicit Feedback

Cited by 4 publications

References 19 publications

Cluster‐based improvement rates for trust establishment models in single or distributed multi‐agent systems

Cluster‐based improvement rates for trust establishment models in single or distributed multi‐agent systems

Improved Trust Establishment Performance with Transaction-based Preprocessing

Multi-Criteria Trust Establishment for Multi-Agent Systems Based on Fuzzy Logic

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