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Title:Lifted First-Order Probabilistic Inference
Author(s):Braz, Rodrigo de Salvo
Doctoral Committee Chair(s):Roth, Dan
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Computer Science
Abstract:There has been a long standing division in AI between logical symbolic and probabilistic reasoning approaches. While probabilistic models can deal well with inherent uncertainty in many real-world domains, they operate on a mostly propositional level. Logic systems, on the other hand, can deal with much richer representations, especially first-order ones. In the last two decades, many probabilistic algorithms accepting first-order specifications have been proposed, but in the inference stage they still operate mostly on a propositional level, where the rich and useful first-order structure is not explicit anymore. In this thesis we present a framework for lifted inference on first-order models, that is, inference where the main operations occur on a first-order level, without the need to prop ositionalize the model. We clearly define the semantics of first-order probabilistic models, present an algorithm (FOVE) that performs lifted inference, and show detailed proofs of its correctness. Furthermore, we describe how to solve the Most Probable Explanation problem with a variant of FOVE, and present a new anytime probabilistic inference algorithm, ABVE, meant to generalize the ability of logical systems to gradually process a model and stop as soon as an answer is available.
Issue Date:2007
Description:91 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.
Other Identifier(s):(MiAaPQ)AAI3290183
Date Available in IDEALS:2015-09-25
Date Deposited:2007

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