固定轨道网络中多智能体调度行为的时空分析

S. Agarwal, Günter Wallner, Jeremy Watson, Fabian Beck
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引用次数: 2

摘要

多智能体系统需要智能体之间的协调来解决给定的任务。对于固定轨道网络上的移动,传统的调度算法迄今为止占据主导地位,但对自主和智能代理的兴趣正在增长,因为它们承诺对意外和特殊情况做出更稳健的反应。在本文中,我们研究了Flatland 2020 NeurIPS竞赛的数据,其中列车通过虚拟铁路网移动。我们开发了一个基于时间线的可视化系统,提供了模拟情节中所有列车运行的概览,清楚地暗示了不同阶段、非最佳路线和诸如死锁等问题。该视图与地图视图和图形视图相辅相成,通过高亮显示和同步动画进行交互链接。在地图中定义感兴趣的区域将构建用于详细检查的分析图。比较模式允许在所有视图中对同一铁路网的两个不同情节进行对比。我们与Flatland社区密切合作进行了这项应用研究。确定的分析目标源于对社区关键人物的访谈,而方法本身是根据来自不同背景的专家的反馈在两次迭代中开发出来的。这些反馈,加上对竞赛中获奖作品的分析,证实了最初的分析目标是可以回答的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatio-temporal Analysis of Multi-agent Scheduling Behaviors on Fixed-track Networks
Multi-agent systems require coordination among the agents to solve a given task. For movement on fixed-track networks, traditional scheduling algorithms have dominated so far, but the interest in autonomous and intelligent agents is growing as they promise to react to unexpected and exceptional situations more robustly. In this paper, we study data from the Flatland 2020 NeurIPS Competition, where trains move through a virtual rail network. We developed a timeline-based visualization that provides an overview of all train movements in a simulated episode, clearly hinting at different phases, non-optimal routes, and issues such as deadlocks. This view is complemented with a map view and a graph view, interactively linked through highlighting and synchronous animation. Defining regions of interest in the map builds an analysis graph for detailed inspection. A comparison mode allows contrasting two different episodes regarding the same rail network across all views. We have conducted this application study in close collaboration with the Flatland community. Identified analysis goals stem from interviews with key persons of the community, while the approach itself was developed in two iterations based on feedback from experts with diverse backgrounds. This feedback, together with an analysis of the winning submissions from the competition, confirms that the initial analysis goals can be answered.
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