CAREER: Thinking that is "just right": Query-Specific Probabilistic Reasoning and its Application to Large-Scale Sensor Networks

职业：认为“恰到好处”：特定于查询的概率推理及其在大规模传感器网络中的应用

• 批准号: 0644225
• 负责人: Carlos Guestrin
• 金额: $ 50万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-12-15 至 2012-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0644225&HistoricalAwards=false
• 关键词: CAREER; Thinking; just; right; Query

Proposal 0644225"CAREER: Thinking that is 'just right': Query-Specific Probabilistic Reasoning and its Application to Large-Scale Sensor Networks"PI: Carlos GuestrinCarnegie-Mellon UniversityThis project develops a novel approach for probabilistic reasoning in complex systems. Whereas most current approaches work by first learning a probabilistic model from data, committing to this model, and then applying probabilistic inference techniques to answer user queries, this project is pursuing a significantly different approach: learn a model specific for the query at hand. This project addresses the problem that complex real-world systems require complex models, and inference in these models can be intractable, thus forcing most practitioners to apply approximate inference techniques that are unstable and inaccurateThis projects aims to demonstrate that many queries can be answered by simple models that enable exact, stable inference. This project will develop algorithms for building such query-specific models, addressing both static and dynamic inference problems, distributed reasoning, and modular or relational query-specific models.This project's general approach, query-specific probabilistic reasoning, enables the efficient solution of many real-world reasoning problems. Specifically, the project addresses practical problems in sensor networks, including: emergency response, surveillance with camera networks and monitoring of large-scale computer systems. Results from this work will be used to develop a publicly available Machine Learning class, including class projects (data), exercises, notes, slides and lecture videos.

提案0644225“职业：思考是'正当'的：特定特定的概率推理及其在大型传感器网络中的应用“ PI：Carlos GuestRincarnegie-Mellon University University University University University University this Project of Complece Systems在复杂系统中开发了一种新的方法。尽管大多数当前方法通过先学习数据，致力于此模型，然后应用概率推理技术来回答用户查询，但该项目正在采用明显不同的方法：学习针对手头查询的模型。该项目解决了复杂的现实世界系统需要复杂模型的问题，并且这些模型中的推断可能是棘手的，因此迫使大多数从业人员应用近似推理技术，这些推理技术是不稳定的，并且不准确的项目项目旨在证明许多查询可以通过启用精确，稳定的，稳定的定义的简单模型来回答许多查询。该项目将开发用于构建此类特定特定模型的算法，解决静态和动态推理问题，分布式推理以及模块化或关系性查询特异性模型。该项目的一般方法，特定的特定概率推理，可以有效地解决许多现实世界中的推理问题。具体而言，该项目解决了传感器网络中的实际问题，包括：紧急响应，使用相机网络的监视以及对大型计算机系统的监视。这项工作的结果将用于开发公开可用的机器学习课程，包括课堂项目（数据），练习，笔记，幻灯片和载玻片视频。