Behaviour Modelling of Social Animals via Causal Structure Discovery and Graph Neural Networks

• URL: http://arxiv.org/abs/2312.14333v1
• Date: Thu, 21 Dec 2023 23:34:08 GMT
• Title: Behaviour Modelling of Social Animals via Causal Structure Discovery and Graph Neural Networks
• Authors: Ga\"el Gendron, Yang Chen, Mitchell Rogers, Yiping Liu, Mihailo Azhar, Shahrokh Heidari, David Arturo Soriano Valdez, Kobe Knowles, Padriac O'Leary, Simon Eyre, Michael Witbrock, Gillian Dobbie, Jiamou Liu and Patrice Delmas
• Abstract summary: We propose a method to build behavioural models using causal structure discovery and graph neural networks for time series. We apply this method to a mob of meerkats in a zoo environment and study its ability to predict future actions.
• Score: 15.542220566525021
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Better understanding the natural world is a crucial task with a wide range of applications. In environments with close proximity between humans and animals, such as zoos, it is essential to better understand the causes behind animal behaviour and what interventions are responsible for changes in their behaviours. This can help to predict unusual behaviours, mitigate detrimental effects and increase the well-being of animals. There has been work on modelling the dynamics behind swarms of birds and insects but the complex social behaviours of mammalian groups remain less explored. In this work, we propose a method to build behavioural models using causal structure discovery and graph neural networks for time series. We apply this method to a mob of meerkats in a zoo environment and study its ability to predict future actions and model the behaviour distribution at an individual-level and at a group level. We show that our method can match and outperform standard deep learning architectures and generate more realistic data, while using fewer parameters and providing increased interpretability.

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