BehavE: Behaviour Understanding through Situation Models for Situation-aware AssistancE

情境模型允许以结构化和整合的方式表示有关人员的领域知识。这些模型后来被用来推理人的行为、需求和援助策略。目前，情况模型要么是手动构建的，要么是自动生成的，只有少数信息源被使用。为了解决这个问题，本项目旨在开发一种通用的方法，用于从各种异质来源生成情境模型。这种方法将使学习不同问题领域的模型成为可能。更准确地说，它将解决以下问题：(1)从异构源中自动提取领域元素和语义；(2)自动将异构知识整合为统一的情景模型；(3)根据观察到的用户偏好自动优化学习模型；(4)长时间自动维护和整理模型，使其代表当前情况；(5)为现实世界问题的情境模型开发一种评估方法。为了实现这一目标，它将结合现有的和新的方法来解决不同的问题，从异构资源中提取知识和模型学习。它们包括用于语义提取和关系发现的监督和非监督技术；利用现有的结构化知识来改进发现的语义，强化学习技术来优化情境模型，以及各种机器学习技术来维护模型和学习模型启发式。为了评估该方法，将学习到的模型应用于来自老年护理和医疗保健领域的现有数据集，并将其与手工制作模型的性能进行比较。提出的方法将允许我们通过用自动提取的模型代替专家知识和人工开发来减少对专家知识和人工开发的需求。如果成功，该方法将减少构建丰富的高质量情景模型所需的时间和资源，以及开发依赖于领域知识的任何系统，以便对给定问题的解决方案进行推理。

Situation models allow representing domain knowledge about persons in a structured and consolidated manner. These models are later used for reasoning about the person's behaviour, needs and assistance strategies. Currently, situation models are either built manually or when generated automatically, only a few information sources are used. To address this problem, this project aims at developing a generalised methodology for generating situation models from various heterogenous sources. This methodology will enable the learning of models for different problem domains. More precisely, it will address the following problems: (1) automatically extracting the domain elements and semantics from heterogenous sources; (2) automatically consolidating the heterogenous knowledge into a unified situation model; (3) automatically optimising the learned model based on observed user preferences; (4) automatically maintaining and curating the model over long periods of time so it represents the current situation; (5) developing an evaluation methodology for situation models for real world problems.To achieve that, it will combine existing and novel methods that address different problems of knowledge extraction and model learning from heterogenous sources. They include supervised and unsupervised techniques for semantics extraction and relations discovery; making use of existing structured knowledge to improve the discovered semantics, reinforcement learning techniques for optimising the situation model, as well as various machine learning techniques for maintaining the model and learning the model heuristics. To evaluate the approach, the learned models are applied to existing datasets from the elderly care and healthcare domains and their performance compared to that of handcrafted models. The proposed approach will allow us to reduce the need of expert knowledge and manual development by replacing it with automatically extracted models. If successful, the approach will reduce the time and resources needed for building rich high quality situation models and for developing any system that relies on domain knowledge in order to reason about the solution of a given problem.