Statistical relational AI (StaRAI) studies the integration of reasoning under uncertainty with reasoning about individuals and relations. The representations used are often called relational probabilistic models. Lifted inference is about how to exploit the structure inherent in relational probabilistic models, either in the way they are expressed or by extracting structure from observations. This book covers recent significant advances in the area of lifted inference, providing a unifying introduction to this very active field.

After providing necessary background on probabilistic graphical models, relational probabilistic models, and learning inside these models, the book turns to lifted inference, first covering exact inference and then approximate inference. In addition, the book considers the theory of liftability and acting in relational domains, which allows the connection of learning and reasoning in relational domains.

Contributors

Babak Ahmadi, Hendrik Blockeel, Hung Bui, Yuqiao Chen, Arthur Choi, Jaesik Choi, Adnan Darwiche, Jesse Davis, Rodrigo de Salvo Braz, Pedro Domingos, Daan Fierens, Martin Grohe, Fabian Hadiji, Seyed Mehran Kazemi, Kristian Kersting, Roni Khardon, Angelika Kimmig, Jacek Kisy?ski, Daniel Lowd, Wannes Meert, Martin Mladenov, Raymond Mooney, Sriraam Natarajan, Mathias Niepert, David Poole, Scott Sanner, Pascal Schweitzer, Nima Taghipour, Guy Van den Broeck

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Yes, you can access An Introduction to Lifted Probabilistic Inference by Guy Van den Broeck,Kristian Kersting,Sriraam Natarajan,David Poole, Michael I. Jordan,Thomas G. Dietterich in PDF and/or ePUB format, as well as other popular books in Computer Science & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.