EAGER: First Order Decision Diagrams for Relational Markov Decision Processes

EAGER：关系马尔可夫决策过程的一阶决策图

• 批准号: 0936687
• 负责人: Roni Khardon
• 金额: $ 7.78万
• 依托单位: Tufts University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0936687&HistoricalAwards=false
• 关键词: EAGER; First; Order; Decision; Diagrams

Decision making under uncertainty in dynamic, structured, and complex environments is a major area of research in AI. Research under this award aims to make novel contributions in this area by developing and expanding a new compact representation known as First Order Decision Diagrams (FODDs)and new learning and planning algorithms based on abstract Markov Decision Processes that have relational structure. The development of such structures, semantics and algorithms is a crucial step towards optimization and planning in complex real-world domains, such as emergency response, product delivery, and other service domains.

在动态、结构化和复杂环境中的不确定性决策是人工智能研究的一个主要领域。该奖项下的研究旨在通过开发和扩展一种新的紧凑表示法（称为一阶决策图（FODDs））以及基于具有关系结构的抽象马尔可夫决策过程的新学习和规划算法，在这一领域做出新的贡献。这种结构、语义和算法的开发是在复杂的现实世界领域（如应急响应、产品交付和其他服务领域）进行优化和规划的关键一步。