Behaviour Modelling of Social Animals via Causal Structure Discovery and
  Graph Neural Networks
Gaël 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 Patrice Delmas
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.