RI: Probabilistic Reasoning with Bounded Computational Resources

RI：有限计算资源的概率推理

• 批准号: 0713166
• 负责人: Adnan Darwiche
• 金额: $ 45万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-10-01 至 2011-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0713166&HistoricalAwards=false
• 关键词: RI; Probabilistic; Reasoning; Bounded; Computational

Proposal 0713166"RI: Probablistic Reasoning with Bounded Computational resources"PI: Adnan DarwicheUCLAABSTRACTProbabilistic modeling and reasoning currently underlie many real-world applications in diverse areas such as the world wide web, medical informatics, robotics, bioinformatics, and information security. This project aims at significantly improving the scale and utility of probabilistic reasoning systems in these application areas, where success has become increasingly dependent on the availability of efficient and accurate probabilistic reasoning systems. The project is focused on a particular class of probabilistic models, known as Bayesian and Markov networks; these are among the most successful models studied by computer scientists and statisticians. This project is concerned with attaining the highest accuracy of reasoning that is feasible under real-world constraints on computational resources. The project is based on new, fundamental discoveries by the PI's group, showing that the efficiency and accuracy of reasoning can be finely controlled by approximating model dependencies in a dynamic fashion driven by user queries. These discoveries have formed the basis of a new semantics, and a concrete realization, of one of the most influential theories of probabilistic reasoning during the last decade, known as generalized belief propagation (GBP). In addition to pursuing the theoretical and practical implications of the new semantics of GBP, the project also aims at producing a comprehensive software system that embodies this novel and practical realization of GBP, with the intent of making it publicly available to the broad scientific community on a web site. It is anticipated that the developed system, with its surrounding theory and practice, will significantly advance the state of the art in probabilistic reasoning, to the point of both allowing new applications to be handled efficiently, and also increasing the scale and scope of existing applications.

Proposal 0713166“RI：Probablistic Reasoning with Bounded Computational Resources“PI：Adnan DarwicheUCLAABSTRACT概率建模和推理目前是许多现实世界应用的基础，这些应用在不同的领域，如万维网、医学信息学、机器人技术、生物信息学和信息安全。该项目旨在显著提高这些应用领域中概率推理系统的规模和效用，这些应用领域的成功越来越依赖于有效和准确的概率推理系统的可用性。该项目专注于一类特殊的概率模型，称为贝叶斯和马尔可夫网络;这些是计算机科学家和统计学家研究的最成功的模型之一。该项目关注的是在现实世界的计算资源限制下实现最高的推理精度。该项目基于PI小组的新的基本发现，表明推理的效率和准确性可以通过以用户查询驱动的动态方式近似模型依赖关系来精细控制。这些发现已经形成了一个新的语义的基础，和具体的实现，在过去的十年中，最有影响力的概率推理理论之一，被称为广义信念传播（GBP）。除了追求GBP新语义的理论和实践意义外，该项目还旨在制作一个综合软件系统，体现GBP的这种新颖和实际实现，目的是在网站上向广大科学界公开提供。预计开发的系统，其周围的理论和实践，将显着推进概率推理的最新技术水平，到允许新的应用程序被有效地处理，并增加现有应用程序的规模和范围。