A Metadata-Driven Approach for Testing Self-Organizing Multiagent Systems

Nathalia Nascimento

doi:10.1109/ACCESS.2020.3036668

2020-01-01

Nathalia Nascimento *

Paulo Alencar

Carlos Lucena

Donald Cowan

DOI：10.1109/ACCESS.2020.3036668

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摘要

En 中文

Multiagent Systems (MASs) have multiple different characteristics, such as autonomy, and asynchronous and social features, which make these systems difficult to understand. Thus, there is a lack of procedures guaranteeing that multiagent systems once implemented would behave as desired. Determining the reliability of such systems is further complicated by 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 an approach based on metadata and the publish-subscribe paradigm to develop test applications that address the process of failure diagnosis in a self-organizing MAS. The novelty of the proposed approach involves its ability to test self-organizing MAS systems in the context of local and global behavior. To illustrate the use of this approach, we developed a self-organizing MAS system based on 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 energy consumption and maintaining the maximum value of 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.

Keyword:

Metadata-oriented testing

publish-subscribe

failure diagnosis

multiagent system

self-organizing

Internet of Things (IoT)

machine learning

neuroevolution