Continuous Decision Diagrams for Machine Learning and Decision-theoretic AI Planning

用于机器学习和决策理论人工智能规划的连续决策图

• 批准号: RGPIN-2016-05705
• 负责人: Sanner, Scott
• 金额: $ 3.35万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634851
• 关键词: Continuous; Decision; Diagrams; Machine; Learning

A key challenge in both Machine Learning and Decision-theoretic AI Planning is the inability of existing methods to efficiently and accurately reason about piecewise continuous functions. Such functions arise in diverse tasks such as preference learning and real-time optimization of traffic signals. For example, in the latter application area, optimal planners must reason about piecewise continuous bursts of traffic flow that occur when signals change. The proposed research program directly attacks this challenge through the further development of continuous decision diagrams and their application to problems ranging from preference learning and elicitation critical for online commerce to optimized traffic signal control critical for highly congested urban environments.

机器学习和决策理论人工智能规划的一个关键挑战是现有方法无法有效和准确地推断分段连续函数。这些功能出现在各种任务中，如偏好学习和交通信号的实时优化。例如，在后一个应用领域，最优规划者必须对信号变化时出现的分段连续交通流进行推理。拟议的研究计划通过进一步发展连续决策图及其应用于从在线商务关键的偏好学习和启发到高度拥挤的城市环境关键的优化交通信号控制等问题，直接应对这一挑战。