面向城市交通场景下协同分布式学习的真实世界模拟器

Andreas Poxrucker, G. Bahle, P. Lukowicz
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引用次数: 8

摘要

集体适应系统中的协作学习是一个活跃的、开放的研究领域。在Allow Ensembles项目中,我们通过一个称为Evolutionary Knowledge的组件来研究这个问题。在这种情况下出现的一个问题是,如果没有实际的现实世界系统,很难研究协作学习的概念。在本文中,我们提出了一个现实世界城市交通系统的模拟工具的概念,该工具被用作研究协作学习的框架。与现有的即用型交通模拟器相比,其目的不是精确模拟微观或宏观交通流模型。相反,它被用来生成数据来训练学习上下文参数及其相互关系的知识模型,这些参数不能从系统的分析描述中推导出来,而是作为系统复杂性的紧急属性产生的。通过模拟,我们希望研究不同协作学习策略对复杂城市交通系统中实体之间不同知识交换模式的涌现性的影响。我们描述了我们的模拟器的需求和应用领域,展示了与现有交通模拟工具的区别,并提出了其概念架构的概述。
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Towards a Real-World Simulator for Collaborative Distributed Learning in the Scenario of Urban Mobility
Collaborative learning in collective adaptive systems is an active, open research area. In the Allow Ensembles project, we investigate this problem by a component called Evolutionary Knowledge. One problem arising in this context is that concepts of collaborative learning can hardly be studied without an actual real-world system. In this paper, we present our concept of a simulation tool of a real-world urban traffic system used as a framework to investigate collaborative learning. In contrast to existing ready-to-use traffic simulators, its purpose is not the accurate simulation of microscopic or macroscopic traffic flow models. Instead, it is used to generate data to train a knowledge model learning context parameters and their interrelations, which cannot be deduced from an analytical description of the system, but arise as emergent properties from the complexity of the system. Using the simulation we want to investigate the effects of different collaborative learning strategies on emergence in a complex urban mobility system applying different knowledge exchange patterns among entities. We describe the need for and the area of application of our simulator, show the differences to existing traffic simulation tools, and present an outline of its conceptual architecture.
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