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https://hdl.handle.net/2142/81772
Description
Title
Lifted First-Order Probabilistic Inference
Author(s)
Braz, Rodrigo de Salvo
Issue Date
2007
Doctoral Committee Chair(s)
Roth, Dan
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Computer Science
Language
eng
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.
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