| Summary: | 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.
|
|---|
