Lifespan AI - Project M2: Lifespan Knowledge Representation

该项目研究了个人日常活动中与疾病有因果关系的能力和缺陷的表示、推理和建模的计算方法。目标是预测个人是否需要支持，需要什么，他们的损伤如何随着时间的推移而发展，以及这如何取决于环境因素，如环境结构或疲劳。为此，我们的项目将设计、实现和检查寿命预测建模引擎（L-PME）的计算原理，这是一个混合知识表示和推理框架，伴随着个人，记录他们的日常活动，并在终身学习过程中维护个人损伤模型。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了认知数字双胞胎，该知识表示捕获了(1)在个人不断变化的情况、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。(3)不同日常活动中行为的潜在相互依赖性，以回答开放查询并预测性能和行为。我们将研究表征、推理和建模方法的基础，将其应用于在日常活动（如摆桌子）中穿着物理虚拟年龄套装的人。这使我们能够在可观察和可控的条件下通过系统地改变物理-虚拟年龄套装的参数来研究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.