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

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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.