论文标题
可以从群体的视频观察中理解的控制器提取
Understandable Controller Extraction from Video Observations of Swarms
论文作者
论文摘要
群的行为来自代理的局部互动及其环境通常被编码为简单规则。通过观看整体群体行为的视频来提取规则可以帮助我们研究和控制自然界的群体行为，或者是由外部演员设计的人造群体。它还可以作为群体机器人技术灵感的新来源。然而，提取此类规则是具有挑战性的，因为群体的新兴特性与他们的局部互动之间通常没有明显的联系。为此，我们开发了一种方法，可以自动从视频演示中提取可理解的群体控制器。该方法使用由比较八个高级群指标的健身函数驱动的进化算法。该方法能够在简单的集体运动任务中提取许多控制器（行为树）。然后，我们对导致不同树木但类似行为的行为进行定性分析。这提供了基于观测值自动提取群体控制器的第一步。
Swarm behavior emerges from the local interaction of agents and their environment often encoded as simple rules. Extracting the rules by watching a video of the overall swarm behavior could help us study and control swarm behavior in nature, or artificial swarms that have been designed by external actors. It could also serve as a new source of inspiration for swarm robotics. Yet extracting such rules is challenging as there is often no visible link between the emergent properties of the swarm and their local interactions. To this end, we develop a method to automatically extract understandable swarm controllers from video demonstrations. The method uses evolutionary algorithms driven by a fitness function that compares eight high-level swarm metrics. The method is able to extract many controllers (behavior trees) in a simple collective movement task. We then provide a qualitative analysis of behaviors that resulted in different trees, but similar behaviors. This provides the first steps toward automatic extraction of swarm controllers based on observations.
