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

提案0644225“职业生涯：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 Carnegie-Mellon University这个项目为复杂系统中的概率推理开发了一种新的方法。尽管大多数当前方法的工作方式是首先从数据中学习一个概率模型，然后致力于这个模型，然后应用概率推理技术来回答用户查询，但这个项目正在追求一种显著不同的方法：学习手头查询的特定模型。这个项目解决的问题是，复杂的现实世界系统需要复杂的模型，而这些模型中的推理可能很难处理，从而迫使大多数从业者应用不稳定和不准确的近似推理技术。本项目旨在证明，许多查询可以通过支持准确、稳定推理的简单模型来回答。该项目将开发构建此类查询特定模型的算法，解决静态和动态推理问题、分布式推理以及模块化或关系型查询特定模型。该项目的通用方法-特定查询概率推理-使许多现实世界的推理问题能够得到有效解决。具体地说，该项目解决传感器网络中的实际问题，包括：紧急反应、使用摄像机网络进行监视以及对大型计算机系统进行监视。这项工作的结果将被用来开发一个公开的机器学习课程，包括课堂项目(数据)、练习、笔记、幻灯片和授课视频。

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.