Many AI problems arising in a wide variety of fields such as machine learning, semantic web, network communication, computer vision, and robotics can elegantly be encoded and solved using probabilistic graphical models. Often, however, we are facing inference problems with symmetries and redundancies only implicitly captured in the graph structure and, hence, not exploitable by efficient inference approaches. A prominent example are probabilistic logical models that tackle a long standing goal of AI, namely unifying first-order logic — capturing regularities and symmetries — and probability — capturing uncertainty. Although they often encode large, complex models using few rules only and, hence, symmetries and redundancies abound, inference in them was originally still at the propositional representation level and did not exploit symmetries. This paper is intended to give a (not necessarily complete) overview and invitation to the emerging field of lifted probabilistic inference, inference techniques that exploit these symmetries in graphical models in order to speed up inference, ultimately orders of magnitude.
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