Interpretable Behaviour Analysis with External Structured Knowledge

该项目旨在开发新的可解释的神经模型，用于人类行为的预测分析任务，操作与外部支持结构化知识相关的序列行为数据。预计它将为异构行为数据和可解释的机器推理模型的鲁棒表示学习提供理论基础，这些模型可以支持广泛的智能系统。预期成果将是下一代可解释的行为分析系统，具有多种推理能力，可以对各种数据结构进行推理，并提供有关其推理过程的高级可解释性。这些好处将跨越研究和工业部门，例如，零售、医疗保健、服务提供商。

This project aims to develop novel interpretable neural models for predictive analytics tasks on human behaviour, operating on sequence behaviour data associated with external supportive structured knowledge. It is expected to present theoretical foundations for robust representation learning on heterogeneous behaviour data and interpretable machine reasoning models, which can support a broad scope of intelligent systems. Expected outcomes will be a next-generation interpretable behaviour analysis system with versatile abilities to reason over various data structures and provide a high-level interpretability about its reasoning procedure. The benefits will span the research and industry sectors, e.g., retail, healthcare, service provider.