Testing Self-Organizing Multiagent Systems
This addresses the lack of testing procedures for self-organizing MASs, which is a domain-specific problem for researchers and developers in multiagent systems and IoT.
The paper tackles the problem of testing self-organizing multiagent systems (MASs), which are difficult to guarantee due to non-deterministic features like learning and self-organization, by presenting a publish-subscribe-based approach for failure diagnosis, illustrated with a smart street lights simulation in IoT that achieved goals like reducing energy consumption and maintaining visual comfort.
Multiagent Systems (MASs) involve different characteristics, such as autonomy, asynchronous and social features, which make these systems more difficult to understand. Thus, there is a lack of procedures guaranteeing that multiagent systems would behave as desired. Further complicating the situation is the fact that current agent-based approaches may also involve non-deterministic characteristics, such as learning, self-adaptation and self-organization (SASO). Nonetheless, there is a gap in the literature regarding the testing of systems with these features. This paper presents a publish-subscribe-based approach to develop test applications that facilitate the process of failure diagnosis in a self-organizing MAS. These tests are able to detect failures at the global behavior of the system or at the local properties of its parts. To illustrate the use of this approach, we developed a self-organizing MAS system based on the context of the Internet of Things (IoT), which simulates a set of smart street lights, and we performed functional ad-hoc tests. The street lights need to interact with each other in order to achieve the global goals of reducing the energy consumption and maintaining the maximum visual comfort in illuminated areas. To achieve these global behaviors, the street lights develop local behaviors automatically through a self-organizing process based on machine learning algorithms.