Agent-Based Intelligent Decision Support Systems: A Systematic Review

• Published in: IEEE transactions on cognitive and developmental systems Vol. 14; no. 1; pp. 20 - 34
• Main Authors: Khemakhem, Faten, Ellouzi, Hamdi, Ltifi, Hela, Ayed, Mounir Ben
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Bibliographies; Complexity theory; Data mining; Decision making; Decision support systems; Distributed environment; intelligent decision support system (IDSS); Multi-agent systems; multiagent system (MAS); Multiagent systems; Neural networks; SWOT analysis; systematic literature review; Systematic review; Systematics; Task complexity
• ISSN: 2379-8920; 2379-8939
• DOI: 10.1109/TCDS.2020.3030571

Decision-making complexity, in a distributed environment, is due to hard tasks that a system must resolve. This complexity makes researchers focus on looking for solutions to cope with distributed environment problems. Multiagent system (MAS) technology is one of several proposed solutions. This technology rose the challenge in decision-making applications. To date, no systematic review has been conducting, to the best of the authors' knowledge, to give an overview of a multiagent-based decision-making system in various areas of science or technology. In this study, we review of 58 studies published from 2007 to 2019. The aim of this article is a critical analysis of recent approaches. We try to survey their impact on practice and research. The analysis of the extracted studies is based on three selection criteria that are defined in the paper. All included studies have analyzed from four different points of view: 1) theoretical view; 2) technical view; 3) agent view; and 4) application view. Moreover, we adopt the SWOT analysis to evaluate studied approaches. Multiagent technology is actually in the process of evolution and enhancement with the appearance of new trends in artificial intelligence, such as neural network and deep learning. The results of this review show suggestions for further research and practice.