Abstract : The project's main focus was on tractable inference and learning of probabilistic representations, which are essential for large-scale abductive inference applications. We also developed novel inference techniques based on lifting, sampling, and more efficient processing of evidence. We continued to extend Alchemy 2.0, an open-source toolkit for Markov logic, and Alchemy Lite, an implementation of Tractable Markov Logic (TML). We developed parameter and structure learning algorithms for sum-product networks and, building on TML, we substantially improved two tractable probabilistic-logical formalisms: relational sum-product networks and tractable probabilistic knowledge bases. Based on sum-product networks, we worked towards formalisms for tractable probabilistic programming. We worked on symmetrybased inference and learning and developed novel model classes that exploit invariances of the data with respect to group operations. A novel model for Biomedical event extraction based on MLNs that leverages the power of support vector machines (SVMs) to handle highdimensional features was proposed and applied to the problem of event extraction. We developed structured prediction models by introducing novel forms of regularization. We continued to apply Markov logic networks to the problem of textual inference and conducted extensive experiments on benchmark datasets. We further improved GraphLab, our large-scale parallel machine learning framework. We investigated novel approaches to activity and plan recognition, and showed that Markov logic is capable of fusing visual and language evidence of the activities under consideration.