Abstract
Abstract : The goal of this effort was to provide a unified probabilistic framework that integrates symbolic and sensory reasoning. Such a framework would allow sensor data to be analyzed in terms of high-level symbolic models. It will also allow the results of high-level analysis to guide the low-level sensor interpretation task and to help in resolving ambiguities in the sensor data. Our approach was based on the framework of probabilistic graphical models, which allows us to build systems that learn and reason with complex models, encompassing both low-level continuous sensor data and high-level symbolic concepts. Over the five years of the project, we explored two main thrusts: Inference and learning in hybrid and temporal Bayesian networks Mapping and modeling of 3D physical environments. Our progress on each of these two directions is detailed in the attached report.
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