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不确定性下的决策是AI的主要研究领域。根据该奖项的研究旨在通过开发和扩展新的紧凑型代表性（FODD）和基于具有关系结构的抽象决策过程的新的学习和计划算法来在这一领域做出新的贡献。此类结构，语义和算法的开发是在复杂的现实世界中的优化和计划（例如紧急响应，产品传递和其他服务领域）的关键步骤。