Статья

Multi-Dimensional Trust Quantification by Artificial Agents Through Evidential Fuzzy Multi-Criteria Decision Making

Sang-Woong LeePattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam, 13557, South KoreaSadaf HussainSchool of Computer Science, National College of Business Administration & Economics, Lahore, 54000, PakistanGhassan F. IssaSchool of Information Technology, Skyline University College, University City Sharjah, 1797, Sharjah, UAESagheer AbbasSchool of Computer Science, National College of Business Administration & Economics, Lahore, 54000, PakistanTaher M. GhazalSchool of Information Technology, Skyline University College, University City Sharjah, 1797, Sharjah, UAE and Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, MalaysiaTanweer SohailDepartment of Mathematics, University of Jhung, Jhung, PakistanMunir AhmadSchool of Computer Science, National College of Business Administration & Economics, Lahore, 54000, PakistanMuhammad Adnan KhanPattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam, 13557, South Korea. (e-mail: [email protected])

2021en

ABI

Аннотация

Increasing man-machine trust has burgeoned during the last few decades. The growing interest in trust-building has led to the study of the non-dichotomous nature of trust. Trust as social behavior is an integral part of effective team building. The major focus has been offered to study how humans build trust towards machines, whereas few attempts have been made to study the reverse. Studies have shown that trustworthiness perceptions initialize trust behavior whereas trust behavior influences subsequent trustworthiness perceptions. This paper presents the design and comparative analysis of evidential fuzzy multi-criteria decision-making (EFMCDM) based on multi-dimensional trust quantification schemes to quantify trust level with the human agent in a collaborative environment.

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Идентификаторы

DOI: 10.1109/access.2021.3131521

Цитирования и источники

Цитирований: 5Использованных источников: 0

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