A hybrid model for simulating grazing herds in real time

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.

A novel jigsaw game with eye‐tracking: A multimodel interaction based on psycholinguistics for ADHD therapeutic

“…Building on this, we expect future research to apply AR/VR to ADHD patients, coupled with eye‐tracking assessments. Specifically, the hybrid virtual herd model proposed by Demšar et al 24 could be implemented as an immersive digital therapy using augmented reality and eye‐tracking to engage and improve focus in children with ADHD. We believe the various innovative applications of AR/VR as digital therapeutics will bring new opportunities for ADHD diagnosis and intervention.…”