Causal Discovery from Unstructured Data

从非结构化数据中发现因果关系

• 批准号: DE210101624
• 负责人: Dr Mingming Gong
• 金额: $ 28.89万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2021
• 资助国家: 澳大利亚
• 起止时间: 2021-02-01 至 2024-05-10
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE210101624
• 关键词: Causal; Discovery; Unstructured; Data

This Project aims to enable machines to discover causal relations from various kinds of unstructured data, such as images, text files, and sensor data. The project expects to promote causal revolution of data-centric intelligence and science – construct machines that can communicate in the language of cause and effect and answer ‘why’ questions by inferring from unstructured data. Expected outcomes of this project include theoretical foundations for causal discovery from unstructured data and practical algorithms that drive intelligent machines to make rational decisions in real-world scenarios. This should benefit society and the economy nationally and internationally through the applications of artificial intelligence and data science.

该项目旨在使机器能够从各种非结构化数据（如图像，文本文件和传感器数据）中发现因果关系。该项目预计将促进以数据为中心的智能和科学的因果革命-构建可以用因果语言进行交流的机器，并通过从非结构化数据中推断来回答“为什么”的问题。该项目的预期成果包括从非结构化数据中发现因果关系的理论基础，以及驱动智能机器在现实世界中做出理性决策的实用算法。通过人工智能和数据科学的应用，这应该有利于国内和国际的社会和经济。