An integrated trust establishment model for the internet of agents

Aref, Abdullah; Tran, Thomas

doi:10.1007/s10115-019-01348-z

Cited by 6 publications

(35 citation statements)

References 37 publications

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“…Recently a distributed, multi-criteria trust establishment model that integrates explicit and implicate feedback presented in (Aref and Tran, 2020b). The model, known as Integrated Trust Establishment (ITE), assumes that trustors are willing to provide a single aggregated satisfaction level regarding each transaction, but trustors do not provided the importance or level of satisfaction per criterion.…”

Section: Related Workmentioning

confidence: 99%

“…In this study, we aim to evaluate fuzzy logic based on explicit feedback when it is available, rather than using reinforcement learning as in (Aref and Tran, 2017b;2020b).…”

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%

“…Existing trust establishment models (Aref and Tran, 2020b;2017b;2015b;2015c;Tran et al, 2014;Burnett et al, 2011) have been analyzed using simulation to evaluate their effectiveness under different conditions, as obtaining comprehensive real-world data sets can be challenging. We are currently not aware of any agreed-upon benchmark that enables comparing various performance results, as simulation analysis differs among different works.…”

Section: Performance Analysismentioning

confidence: 99%

“…To get more insight on the performance of the proposed model, we compare it with the most relevant work that uses explicit feedback known as MCTE (Aref and Tran, 2017b), the most recent work that uses both implicit and explicit feedback known as ITE (Aref and Tran, 2020a) and the wildly compared with model; the RI (Burnett et al, 2011).…”

Section: Performance Analysismentioning

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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Section: Related Workmentioning

confidence: 99%

“…In this study, we aim to evaluate fuzzy logic based on explicit feedback when it is available, rather than using reinforcement learning as in (Aref and Tran, 2017b;2020b).…”

Section: Related Workmentioning

confidence: 99%