Sequential decision-making in dynamic and uncertain environments

动态和不确定环境中的顺序决策

• 批准号: DP200100700
• 负责人: Prof Jie Lu
• 金额: $ 33.7万
• 依托单位: University of Technology Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2020
• 资助国家: 澳大利亚
• 起止时间: 2020-06-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP200100700
• 关键词: Sequential; decision; making; dynamic; uncertain

Current machine learning and optimisation methods cannot well support sequential prediction and decision-making due to the dynamic nature and pervasive presence of big data. This project aims to create a foundation and technology for sequence and uncertainty learning, sequential and dynamic optimisation, and their integration. It is expected to improve robustness and mitigate the vulnerabilities of machine learning algorithms, to increase prediction accuracy and reliability in dynamic sequences, and to support decision-making in complex situations to achieve robust and adaptive results. Anticipated outcomes can help data scientists with state-of-the-art skills to manage sequential data and benefit data-enabled innovation in Australia.

由于大数据的动态性和普遍存在，当前的机器学习和优化方法不能很好地支持顺序预测和决策。该项目旨在为序列和不确定性学习、序列和动态优化及其集成创建基础和技术。预计它将提高机器学习算法的鲁棒性并减轻其脆弱性，提高动态序列中的预测准确性和可靠性，并支持复杂情况下的决策，以实现鲁棒和自适应的结果。预期的结果可以帮助具有最先进技能的数据科学家管理序列数据，并使澳大利亚的数据创新受益。