RI: Small: Generalized Anytime Probabilistic Inference

RI：小：广义随时概率推理

• 批准号: 1118122
• 负责人: Adnan Darwiche
• 金额: $ 44.94万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-10-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1118122&HistoricalAwards=false
• 关键词: RI; Small; Generalized; Anytime; Probabilistic

Probabilistic reasoning is now routinely used in many fields of science and engineering, where it underlies systems that perform tasks such as text classification, social network analysis, medical diagnosis, information extraction, probabilistic planning, vision and robotics. This project aims to develop an anytime and generalized probabilistic inference engine that targets a wide range of probabilistic representations, including classical, propositional representations --- such as Bayesian and Markov networks --- in addition to more expressive representations based on first order logic. The project will also investigate probabilistic queries in complexity classes that have not received much attention in the literature, yet can be used to study the robustness of inferences and decisions based on probabilistic reasoning systems. The targeted inference engine is planned to put the state of the art in exact inference at the service of approximate inference, allowing it to resign to approximations only when exact inference yields. Moreover, the engine is planned to smoothly and incrementally improve its approximations over time. A key emphasis of the project is to accomplish these objectives while using the most general probabilistic representation as an input, to allow for the widest possible adoption of the developed inference engine. Our anticipated results will impact many fields by allowing users to perform more accurate probabilistic inference, on larger models and in different contexts. Through scientific articles, research seminars, conference presentations, and graduate teaching, we expect the obtained results to be widely disseminated so as to maximize the attained benefits by various communities. Moreover, we plan to publicly release software systems that embed our results, continuing with a long tradition of publicly releasing award-winning software systems for probabilistic reasoning.

概率推理现在经常用于许多科学和工程领域，它是执行文本分类，社交网络分析，医疗诊断，信息提取，概率规划，视觉和机器人等任务的系统的基础。该项目旨在开发一个随时和广义概率推理引擎，其目标是广泛的概率表示，包括经典的命题表示-如贝叶斯和马尔可夫网络-以及基于一阶逻辑的更具表现力的表示。该项目还将研究在文献中没有得到太多关注的复杂性类中的概率查询，但可以用于研究基于概率推理系统的推理和决策的鲁棒性。目标推理引擎计划将最先进的精确推理服务于近似推理，仅当精确推理产生时才允许它辞职近似。此外，该引擎计划随着时间的推移平稳和渐进地改进其近似值。该项目的一个重点是实现这些目标，同时使用最一般的概率表示作为输入，以允许尽可能广泛地采用所开发的推理引擎。我们的预期结果将通过允许用户在更大的模型和不同的背景下执行更准确的概率推理来影响许多领域。通过科学文章，研究研讨会，会议演示和研究生教学，我们希望所获得的成果得到广泛传播，以最大限度地提高各社区的利益。此外，我们计划公开发布嵌入我们的结果的软件系统，继续公开发布屡获殊荣的概率推理软件系统的悠久传统。