EAGER: Model-Free Classification of Collective Behavior Based on Automated Detection of Symmetry from Video Data

这个早期概念探索性研究奖助金(EIGER)项目将动物群体中的紧急动力学专家和自动科学发现专家聚集在一起。自动发现涉及在观察数据中搜索潜在的模式--具体地说，搜索对称性。对称性是指在重新定位或重新定向的情况下保持不变的性质，许多基本的物理定律表现出某种类型的对称性。这个项目将扩展在测量的运动中寻找对称性的方法，以应用于原始视频数据。这一扩展将应用于动物单独或成群、成群或成群移动的视频。这些结果将被用来理解集体运动如何与个体运动相关和不同，以及哪些共同因素与不同动物的集体运动有关。研究结果将对工程多智能体系统的安全和有效性能具有价值，如自动驾驶汽车排、与人类群体互动的机器人和成群的四旋翼无人机。该项目旨在开发一种定量方法，使用自动发现算法来区分和测量多智能体系统中的集体行为，该算法基于系统的动态对称性定义，并已被用于评估一对时间序列是否为相同控制动力学的实现。现有的算法可以分析耦合动力系统中的时间序列。对于这个项目，该算法将应用于来自动物的数据，无论是单独的还是成组的。为了适应最广泛使用的数据形式，该算法将进行修改，直接将原始视频作为输入。该算法将被用来探索选定的数值模型中的个人行为与集体行为，以及来自生物群的视频数据。最后，该算法将用于多物种数据集，以量化地关联不同物种和行为的动物群体及其模型。这里创建的方法将构成一个强大的模型验证协议，并将提供对动物群体表现出的集体行为之间的差异的洞察，无论是在物种内部还是物种之间。

This EArly-concept Grant for Exploratory Research (EAGER) project brings together an expert in emergent dynamics in animal collectives with an expert in automated scientific discovery. Automated discovery concerns the search for underlying patterns -- specifically, the search for symmetry -- in observed data. Symmetry refers to the property of remaining unchanged under relocation or reorientation, and many fundamental physical laws exhibit some type of symmetry. This project will extend methods for finding symmetries in measured motion to apply to raw video data. This extension will then be applied to videos of animals moving singly and in flocks, schools, or swarms. The results will be used to understand how collective motion is related to, and different from, individual motion, and which common factors relate collective motion in different animals. The results will have value for safe and effective performance of engineered multi-agent systems, such as platoons of self-driving cars, robots interacting with human groups, and swarms of quadrotor drones.This project seeks to develop a quantitative method to discriminate and measure collective behavior in multi-agent systems using an automated discovery algorithm, based on "dynamical kinds," which are defined in terms of the dynamic symmetries of a system, and have been used to assess whether a pair of time series are realizations of the same governing dynamics. The existing algorithm can analyze time series from coupled dynamical systems. For this project, the algorithm will be applied to data from animals, individually and in groups. To accommodate the most widely available form of data, the algorithm will be modified to take raw video directly as input. The algorithm will be used to explore individual versus collective behavior in selected numerical models, and in video data from biological swarms. Finally, the algorithm will be used across a multi-species dataset, to quantitatively relate animal groups and their models across species and behaviors. The method created here will constitute a powerful protocol for model validation, and will offer insight into differences between collective behaviors exhibited by animals groups, both within and between species.