AIC Seminar Series
Lifted First-Order Probabilistic Inference
|Rodrigo de Salvo Braz||University of Illinois at Urbana-Champaign|
Notice: hosted by Richard Waldinger
Date: 2007-02-20 at 16:00
Location: EJ228 (Directions)
Logic and Probabilistic Inference are the two main inference models
available today in Artificial Intelligence (AI). Logic, particularly
first-order, is expressive and high-level, but lacks modelling of
probabilistic uncertainty, which is important in many AI applications
such as common sense, natural language processing and vision.
Probabilistic Inference excels in modeling uncertainty and has been a
success story in many areas. However, it lacks expressivity, being
propositional rather than first-order. This prevents us from applying
them to structured data such as collections of graphs, trees and
frames in a convenient manner.
Several languages have been proposed that allow the expression of
probabilistic knowledge in rich first-order languages. However, at
inference time these solutions still operate at a mostly
propositional level, by grounding the original first-order
specification. This is a severe limitation because the number of
propositional random variables will typically be exponential in the
number of objects, and because the first-order specification contains
explicit representation of valuable domain structure that gets lost
in the process.
We present an algorithm that operates directly on the first-order level specification of a model, thus taking advantage of compact first-order structures and being potentially much faster. The algorithm keeps the representation as compact and high-level as possible even during inference. We show how this brings probabilistic inference closer to first-order logical inference methods such as resolution.
Bio for Rodrigo de Salvo Braz||
Rodrigo de Salvo Braz is finishing his Ph.D. at the Department of
Computer Science, University of Illinois at Urbana-Champaign. Before
UIUC he received a Masters in Computer Science from the University of
Sao Paulo and spent two years at the Cognitive and Linguistic
Sciences Department at Brown University. His research interests are
First-Order Probabilistic Inference, Natural Language Processing,
Machine Learning and Human-Computer Interaction.
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