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A Survey of First-Order Probabilistic Models Rodrigo de Salvo Braz , Eyal Amir, and Dan Roth
 

Summary: 12
A Survey of First-Order Probabilistic Models
Rodrigo de Salvo Braz , Eyal Amir, and Dan Roth
Department of Computer Science
University of Illinois at Urbana-Champaign
Urbana, IL 61801
Summary. There has been a long standing division in Artificial Intelligence between
logical 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 rep-
resentations, especially first-order ones, but treat uncertainty only in limited ways.
Therefore, an integration of these types of inference is highly desirable, and many ap-
proaches have been proposed, especially from the 1990s on. These solutions come from
many different subfields and vary greatly in language, features and (when available at
all) inference algorithms. Therefore their relation to each other is not always clear, as
well as their semantics. In this survey, we present the main aspects of the solutions
proposed and group them according to language, semantics and inference algorithm. In
doing so, we draw relations between them and discuss particularly important choices
and tradeoffs.
For decades after the field of Artificial Intelligence (AI) was established, its most

  

Source: Amir, Eyal - Department of Computer Science, University of Illinois at Urbana-Champaign

 

Collections: Computer Technologies and Information Sciences