A hybrid model for simulating grazing herds in real time. (8th August 2019) - Demšar, Jure; Blewitt, Will; Lebar Bajec, Iztok
Abstract: Computer simulations of animal groups are usually performed via individual‐based modelling, where simulated animals are designed on the level of individuals. With this approach, developers are able to capture behavioural nuances of real animals. However, modelling each individual as its own entity has the downside of having a high computational cost, meaning that individual‐based models are usually not suitable for real‐time simulations of very large groups. A common alternative approach is flow‐based modelling, where the dynamics of animal congregations are designed on the group level. This enables researchers to create real‐time simulations of massive phenomena at the cost of biological authenticity. A novel approach called hybrid modelling tries to mix the best of both worlds—precision of individual‐based models and speed of flow‐based ones. An unknown surrounding hybrid model is the question of their biological authenticity and relevance. In this study, we develop a hybrid model for the simulation of herds of grazing sheep. Through Bayesian data analysis, we show that such an approach can encompass several aspects of real‐world sheep behaviour. Our hybrid model is also extremely efficient, capable of simulating herds of more than 1, 000 individuals in real time without resorting to graphics processing unit execution. Abstract : A novel approach for simulating groups of animals called hybrid modelling tries to mix the precision of individual based models with the speed of flow based ones. In the presented study, we developed a hybrid model for simulating herds of grazing sheep. Our simulations showed that a hybrid approach can encompass several aspects of real‐world sheep behaviour while maintaining high efficiency ‐ our model is capable of simulating herds of more than 1000 individuals in real‐time without resorting to GPUs.