CAREER: Explanation, Decision Making, and Learning in Graphical Models

职业：图形模型中的解释、决策和学习

• 批准号: 0953723
• 负责人: Changhe Yuan
• 金额: $ 45.52万
• 依托单位: Mississippi State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-15 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0953723&HistoricalAwards=false
• 关键词: CAREER; Explanation; Decision; Making; Learning

Graphical models, such as Bayesian networks and influence diagrams, provide principled approaches to solving reasoning and decision making under uncertainty problems. However, the adaptability and scalability of existing methods for these graphical models are often limited. This project aims to address some of these limitations by developing new and improved approaches to explanation, decision making, and learning in graphical models. It includes the following specific objectives: (1) developing new approaches to finding explanations that only contain the most relevant variables for given observations in Bayesian networks, (2) developing heuristic search-based methods and algorithms to solve influence diagrams more efficiently, (3) developing new algorithms for learning optimal Bayesian networks guided by domain-specific heuristic information so that only a small fraction of the solution space need to be explored, and (4) applying the methods developed in this project to real-world applications including multiple-fault diagnosis, supply chain risk management, and online collaborative learning. This project can lead to significantly better approaches to reasoning and decision making under uncertainty in many disciplines where graphical models have found successful applications, including medicine, security, planning, business, economics, education, and many others. This project can also lead to the development of new and enhanced courses and curricula, the involvement of students from underrepresented groups in the research, and a wide dissemination of the research outcomes through free software, publications, and presentations.

图形模型，如贝叶斯网络和影响图，提供了解决不确定性问题下的推理和决策的原则性方法。然而，这些图形模型的现有方法的适应性和可扩展性往往是有限的。该项目旨在通过开发新的和改进的方法来解释，决策和学习图形模型来解决这些限制。它包括以下具体目标：（1）开发新的方法来寻找解释，这些解释只包含贝叶斯网络中给定观测的最相关变量，（2）开发基于启发式搜索的方法和算法来更有效地求解影响图，（3）提出了基于领域指导的最优贝叶斯网络学习算法，具体的启发式信息，使只有一小部分的解决方案空间需要探索，（4）应用在这个项目中开发的方法，以现实世界的应用，包括多故障诊断，供应链风险管理，在线协作学习。这个项目可以导致更好的方法来推理和决策下的不确定性，在许多学科中，图形模型已经找到了成功的应用，包括医学，安全，规划，商业，经济学，教育，和许多其他。该项目还可以导致开发新的和增强的课程和课程，参与研究的学生来自代表性不足的群体，并通过免费软件，出版物和演示文稿广泛传播的研究成果。