自组织多智能体系统的测试
Testing Self-Organizing Multiagent Systems
摘要：
多智能体系统具有自治性、异步性和社会性等特征，这些特征使得系统的理解更加困难。因此，缺乏保证多智能体系统按预期运行的程序。使情况进一步复杂化的是，目前基于代理的方法还可能涉及非确定性特征，例如学习、自适应和自组织（SASO）。尽管如此，在文献中有一个缺口，关于测试系统与这些功能。本文提出了一种基于订阅的方法来开发测试应用程序，促进故障诊断过程中的自组织MAS。这些测试能够检测系统的全局行为或其部件的局部属性的故障。为了说明这种方法的使用，我们开发了一个基于物联网（IoT）的自组织MAS系统，它模拟了一组智能路灯，我们进行了功能性的ad-hoc测试。路灯需要相互作用，以实现降低能耗和保持照明区域最大视觉舒适度的总体目标。为了实现这些全局行为，路灯通过基于机器学习算法的自组织过程自动开发局部行为。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
