Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence

• URL: http://arxiv.org/abs/2008.01499v2
• Date: Fri, 7 Aug 2020 06:03:43 GMT
• Title: Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence
• Authors: Yuzhu Wu, Zhen Zhang, Gang Kou, Hengjie Zhang, Xiangrui Chao, Cong-Cong Li, Yucheng Dong and Francisco Herrera
• Abstract summary: We present the taxonomy of existing distributed linguistic representations. We review the key elements of distributed linguistic information processing in decision making. Next, we provide a discussion on ongoing challenges and future research directions from the perspective of data science and explainable artificial intelligence.
• Score: 26.908909011805502
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. To provide a comprehensive perspective on the development of distributed linguistic representations in decision making, we present the taxonomy of existing distributed linguistic representations. Then, we review the key elements of distributed linguistic information processing in decision making, including the distance measurement, aggregation methods, distributed linguistic preference relations, and distributed linguistic multiple attribute decision making models. Next, we provide a discussion on ongoing challenges and future research directions from the perspective of data science and explainable artificial intelligence.

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