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

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

Proposal 0644225 "CAREER: Thinking that is 'just right': Query-Specific Probabilistic Reasoning and its Application to Large-Scale Sensor Networks" PI: Carlos Guestrin Carnegie-Mellon University This 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 inaccurate This 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.