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

行为：通过情境模型理解行为以提供情境感知援助

• 批准号: 433339426
• 负责人: Professorin Dr.-Ing. Kristina Yordanova
• 金额: --
• 依托单位: Institut für Data Science
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/433339426?language=en
• 关键词: BehavE; Behaviour; Understanding; through; Situation

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.

情景模型允许以结构化和统一的方式表示关于人的领域知识。这些模型后来被用来对人的行为、需求和援助策略进行推理。目前，态势模型要么是手动建立的，要么是自动生成的，只使用了很少的信息源。为了解决这个问题，这个项目的目标是开发一种通用的方法，用于从各种不同的来源生成情景模型。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)自动从异质来源提取领域元素和语义；(2)自动将异质知识合并到统一的情景模型中；(3)根据观察到的用户偏好自动优化学习的模型；(4)长期自动维护和管理模型，使其代表当前情况；(5)开发针对现实世界问题的情景模型的评估方法。为了实现这一点，它将结合现有的和新的方法，解决不同问题的知识提取和模型学习。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；用于优化情境模型的强化学习技术；以及用于维护模型和学习模型启发式的各种机器学习技术。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，并将其性能与手工制作的模型进行比较。拟议的方法将使我们能够减少对专业知识和手动开发的需要，用自动提取的模型取代它。如果成功，这种方法将减少建立丰富的高质量情景模型和开发任何依赖领域知识的系统以推理给定问题的解决方案所需的时间和资源。