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‘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”PI：Carlos Guestrin 卡内基梅隆大学该项目开发了一种在复杂系统中进行概率推理的新颖方法。虽然当前大多数方法的工作原理是首先从数据中学习概率模型，致力于该模型，然后应用概率推理技术来回答用户查询，但该项目正在追求一种截然不同的方法：学习特定于当前查询的模型。该项目解决了复杂的现实世界系统需要复杂模型的问题，而这些模型中的推理可能很棘手，从而迫使大多数从业者应用不稳定且不准确的近似推理技术。该项目旨在证明许多查询可以通过简单的方法来回答能够实现精确、稳定推理的模型。该项目将开发用于构建此类特定于查询的模型的算法，解决静态和动态推理问题、分布式推理以及模块化或关系特定于查询的模型。该项目的通用方法，即特定于查询的概率推理，可以有效解决许多问题现实世界的推理问题。具体来说，该项目解决了传感器网络中的实际问题，包括：应急响应、摄像头网络监控以及大型计算机系统监控。这项工作的结果将用于开发公开的机器学习课程，包括课程项目（数据）、练习、笔记、幻灯片和讲座视频。