Lifted First-Order Probabilistic Inference

Braz, Rodrigo de Salvo

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https://hdl.handle.net/2142/11362 Copy

Description

Title

Lifted First-Order Probabilistic Inference

Author(s)

Braz, Rodrigo de Salvo

Issue Date

2007-09

Keyword(s)

artificial intelligence

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 propositionalize 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.

Type of Resource

text

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http://hdl.handle.net/2142/11362

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