Behavioural models in sequential inference about spatio-temporal structures

时空结构顺序推理的行为模型

• 批准号: 2108272
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2108272
• 关键词: Behavioural; models; sequential; inference; about

Project Summary: The project will involve new models and scalable inference methods for dynamic learning of behaviours and intentionality in multiple interacting objects, e.g. animals hunting prey, sea mammals/fish interacting in schools/shoals, aircraft or birds interacting in formation and high frequency financial modelling of multiple order books. We will provide new methodologies for scientists and zoologists wishing to test/develop hypotheses about behavioural interactions, new methods for improved situational awareness/intent prediction in general tracking applications (civilian or military), and scalable inference in larger scale problems than currently manageable. The key objective of the project is to improve upon current state-of-the-art tracking algorithms (particle filters) for analysing interactions between multiple objects. One particular problem of interest is to reduce the complexity of the algorithm model such that large groups of interacting objects can be analysed with limited computational cost, where current methods are too slow.The methodology will use multiple dimensional stochastic processes in continuous time. The underlying principles will be Bayesian updating of dynamically evolving objects, implemented using novel scalable combinations of Sequential Monte Carlo, message passing and machine learning algorithms. New probabilistic models of behavioural interactions will be employed within the existing tracking framework to incorporate inference of characteristics such as intention, future behaviour, health and leadership.This project has applications in many domains of national and international importance, including the monitoring of wildlife (changes in arctic seal behaviours/ populations, sea mammals, fish), monitoring of large scale spatio-temporal effects in climate change, enhanced situational awareness in defense applications.

项目概述：该项目将涉及新的模型和可扩展的推理方法，用于动态学习多个相互作用对象的行为和意向性，例如动物狩猎猎物，海洋哺乳动物/鱼类在鱼群/鱼群中相互作用，飞机或鸟类在信息中相互作用，以及多个订单簿的高频金融建模。我们将为科学家和动物学家提供新的方法，希望测试/开发关于行为相互作用的假设，在一般跟踪应用（民用或军用）中改进态势感知/意图预测的新方法，以及在比目前更大的规模问题中进行可扩展的推理。该项目的主要目标是改进当前最先进的跟踪算法（粒子过滤器），以分析多个物体之间的相互作用。一个特别有趣的问题是降低算法模型的复杂性，这样就可以用有限的计算成本分析大量相互作用的对象，而当前的方法太慢了。该方法将使用连续时间的多维随机过程。基本原理将是动态发展对象的贝叶斯更新，使用顺序蒙特卡罗，消息传递和机器学习算法的新颖可扩展组合实现。将在现有的跟踪框架内采用新的行为相互作用概率模型，以纳入对意图、未来行为、健康和领导等特征的推断。该项目应用于许多具有国家和国际重要性的领域，包括监测野生动物（北极海豹行为/种群、海洋哺乳动物、鱼类的变化）、监测气候变化的大尺度时空效应、增强国防应用中的态势感知。