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

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...
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Autor*in:	Nathalia Nascimento [verfasserIn] Paulo Alencar [verfasserIn] Carlos Lucena [verfasserIn] Donald Cowan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Metadata-oriented testing publish-subscribe failure diagnosis multiagent system self-organizing Internet of Things (IoT)

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 204256-204267
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:204256-204267

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.3036668

Katalog-ID:	DOAJ05268413X

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callnumber-first	T - Technology
author	Nathalia Nascimento
spellingShingle	Nathalia Nascimento misc TK1-9971 misc Metadata-oriented testing misc publish-subscribe misc failure diagnosis misc multiagent system misc self-organizing misc Internet of Things (IoT) misc Electrical engineering. Electronics. Nuclear engineering A Metadata-Driven Approach for Testing Self-Organizing Multiagent Systems
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topic_title	TK1-9971 A Metadata-Driven Approach for Testing Self-Organizing Multiagent Systems Metadata-oriented testing publish-subscribe failure diagnosis multiagent system self-organizing Internet of Things (IoT)
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title	A Metadata-Driven Approach for Testing Self-Organizing Multiagent Systems
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title_sort	metadata-driven approach for testing self-organizing multiagent systems
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title_auth	A Metadata-Driven Approach for Testing Self-Organizing Multiagent Systems
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	A Metadata-Driven Approach for Testing Self-Organizing Multiagent Systems
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