Abstract
The application of Artificial Intelligence mechanisms allows the development of systems capable to solve very complex engineering problems. Multi-agent systems (MAS) are one paradigm that allows an alternative way to design distributed control systems. While research in this area grew exponentially before 2009, there is a need to understand the status quo of the field from 2009 to June 2017. An extension of the results of a SLR related to Multi-Agent Systems, its applications and research gaps, following Kitchenham and Wholin guidelines are presented in this paper. From the analysis of 279 papers (out of 3522 candidates), our findings suggest that: a) there were 20 gaps related to agent-oriented methodologies; coordination, cooperation and negotiation; modelling, developing, testing and debugging; b) 24 gaps related to specific domains (recycling, dynamic evacuation, hazard management, health-care, industry, logistics and manufacturing, machine learning, ambient assisted living); and 14 gaps related to specific areas within MAS (A-Teams, dynamic MAS and mobile agents, ABMS, evolutionary MAS, and self-organizing MAS). These gaps specify lines of research where the MAS community must work to achieve the unification of the agent-oriented paradigm; as well as strengthen ties with the industry.
Highlights
Over two decades ago, agents and Multi-Agent Systems (MAS) became a novel way of conducting the analysis, design, model and implementation of complex software systems [1]
The present study is an extension of another work by the authors [14]; and in this case, the focus was placed on the research gaps, reviewing the selected articles again and incorporating a greater number of gaps, both general and specific, into the tables; which is a huge contribution to the MAS community, achieving 20 gaps related to agent-oriented methodologies; coordination, cooperation and negotiation; modelling, developing, testing and debugging; b) 24 gaps related to specific domains; and 14 gaps related to specific areas within MAS (A-Teams, dynamic MAS and mobile agents, ABMS, evolutionary MAS, and self-organizing MAS)
According to the goal presented, we formulate the following set of Research Questions (RQs), where Research Question 1 (RQ1) concerns to the general objective, RQ2 and RQ3 allow identifying and characterizing authors and communities, while RQ4 to RQ6 address topics related to applications, and RQ7 allows the identification of SMA gaps
Summary
Agents and Multi-Agent Systems (MAS) became a novel way of conducting the analysis, design, model and implementation of complex software systems [1]. Agents can be defined as computer systems, autonomous, and they can exhibit a flexible behaviour (to be reactive, proactive and to achieve social ability) [2]. This definition is extended by the Belief-Desire-Intention (BDI) notion [3]. We followed the guideline provided by Kitchenham [12], complemented with the snowballing approach [13], which begin with a set of relevant papers which will be later analyse by their citations and references in order to add papers to the original set. From the four original papers, we obtained 3522 articles through the application of the snowballing approach, and after the inclusion/exclusion criteria, only 279 articles remained
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