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oai:www.ideals.illinois.edu:2142/81772 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Roth, Dan Braz, Rodrigo de Salvo 2015-09-25T20:20:24Z 2015-09-25T20:20:24Z 10000-01-01 2007 2007 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. Made available in DSpace on 2015-09-25T20:20:24Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3290183.pdf: 3025418 bytes, checksum: fd5cb6275af1aa773194b5bd9b85d93f (MD5) Previous issue date: 2007 Embargo set by: Seth Robbins for item 83053 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 91 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007. http://hdl.handle.net/2142/81772 (MiAaPQ)AAI3290183 eng Computer Science Lifted First-Order Probabilistic Inference text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.