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.

翻译：更深入地理解自然界是一项具有广泛应用前景的关键任务。在动物园等人与动物近距离接触的环境中，阐明动物行为背后的原因及引发行为变化的干预因素至关重要。这有助于预测异常行为、减轻不利影响并提升动物福祉。现有研究已涉及鸟群和昆虫群体的动态建模，但哺乳动物群体的复杂社会行为仍待深入探索。本研究提出一种融合因果结构发现与图神经网络的方法，用于时间序列数据的行为建模。我们将该方法应用于动物园环境中猫鼬群体的行为建模，评估其在个体层面与群体层面上预测未来行为、模拟行为分布的能力。实验表明，该方法能以更少的参数量、更强的可解释性匹配甚至超越标准深度学习架构的性能，并生成更符合现实的数据。