Lifespan AI - Project M2: Lifespan Knowledge Representation

该项目研究了与疾病有因果关系的个人日常活动的能力和障碍的表示，推理和建模的计算方法。目标是预测个人是否需要支持，以及需要什么，他们的损伤如何随着时间的推移而进展，以及这如何取决于环境因素，如环境或疲劳的结构。为此，我们的项目将设计，实现和研究寿命预测建模引擎（L-PME）的计算原理，这是一种混合知识表示和推理框架，伴随个人，记录他们的日常活动，并在终身学习过程中维护个人模型。为了代表个人，L-PME从日常活动的通用损伤感知知识表示中定制认知数字孪生模型，该知识表示捕获（1）在人不断变化的情况，需求和任务的背景下疾病引起的损伤的发展，（2）能力和损伤对日常操作任务的因果影响，以及（3）在不同的日常活动中，回答开放性问题和预测表现和行为的行为之间潜在的相互依赖性。我们将调查的基础上的代表性，推理和建模方法，通过将其应用到人穿着物理虚拟年龄西装在日常活动中，如设置表。这使我们能够通过在专家在环过程中系统地改变物理-虚拟年龄套装的参数，在可观察和可控制的条件下研究L-PME的原理。

This project investigates computational methods for representation, reasoning, and modelling of capabilities and impairments, causally related to diseases, of individuals performing everyday activities. The goal is to make predictions about whether individuals need support and for what, how their impairments progress over time, and how this depends on contextual factors, like the structuring of the environment or fatigue. To this end, our project will design, realise, and examine the computational principles of a Lifespan Predictive Modelling Engine (L-PME), a hybrid knowledge representation and reasoning framework that accompanies individuals, records their everyday activities, and maintains individual models of impairments in a lifelong learning process. To represent an individual person, L-PME customises a cognitive digital twin from a generic impairment-aware knowledge representation of everyday activities that captures (1) the development of disease-induced impairments in context of a person's changing situations, needs, and tasks, (2) the causal influence of capabilities and impairments on everyday manipulation tasks, and (3) the latent interdependence of behaviours in different everyday activities to answer open queries and predict performance and behaviour. We will investigate the foundations of the representation, reasoning, and modelling methods by applying it to persons wearing physical-virtual age suits during everyday activities, such as setting the table. This allows us to investigate the principles of L-PME under observable and controllable conditions by changing the parameters of the physical-virtual age suits systematically in an expert-in-the-loop process.