A Generalised Approach to Learning Models of Human Behaviour for Activity Recognition from Textual Instructions

活动识别的计算模型旨在根据前提-效果规则识别用户的动作和目标。这些方法面临的一个问题是如何获得模型结构。为了减少建模过程中对领域专家或传感器数据的需求，研究了从文本数据学习人类行为模型的方法。然而，现有的方法在学习过程中做出了各种简化的假设。这使得该模型不适用于活动识别问题。为了解决这个问题，这个项目旨在开发一种通用的方法，用于从文本说明中学习模型结构。该方法应结合现有的和新的模型学习方法。-应开发一种从文本中提取动作语义的方法。该方法应解决在具有简短和简单的句子结构的文本中识别元素之间的因果关系的挑战。-为确保模型泛化和并入上下文信息，应研究本体学习的方法。这里的核心挑战是基于问题域中实体的因果、空间和功能属性的本体扩展。-为了学习模型语义，应该研究语言基础的方法。语义应按照前提-效果规则来表示。由此产生的方法应解决与学习这些规则并将其转换为适合活动确认的格式相关的问题。它还将解决通过强化学习方法学习最优模型的问题。-为了评估该方法，应将学习的模型应用于各种活动识别任务，并将其性能与手工制作的模型进行比较。如果成功，该方法将减少开发用于活动识别的人类行为计算模型所需的时间和资源。

Computational models for activity recognition aim at recognising the user actions and goals based on precondition-effect rules. One problem such approaches face, is how to obtain the model structure. To reduce the need of domain experts or sensor data during the model building, methods for learning models of human behaviour from textual data have been investigated. Existing approaches, however, make various simplifying assumptions during the learning process. This renders the model inapplicable for activity recognition problems. To address this problem, this project aims at developing a generalised methodology for learning the model structure from textual instructions. The methodology shall combine existing and novel methods for model learning.- A methodology for extracting the action semantics from text shall be developed. The methodology shall address the challenge of identifying causal relations between elements in texts with short and simple sentence structure.- To ensure the model generalisation and the incorporation of context information, methods for ontology learning shall be investigated. A core challenge here is the ontology extension based on causal, spatial, and functional properties of the entities in the problem domain.- To learn the model semantics, methods for language grounding shall be investigated. The semantics shall be represented in terms of precondition-effect rules. The resulting methodology shall address the issues associated with learning these rules and translating them into an appropriate for activity recognition format. It shall also address the problem of learning an optimal model through reinforcement learning methods.- To evaluate the methodology, the learned models shall be applied to various activity recognition tasks and their performance compared to that of handcrafted models.If successful, the methodology will reduce the time and resources needed for developing computational models of human behaviour for activity recognition.