Lifted first-order probabilistic inference

Authors:
Rodrigo De Salvo Braz;Eyal Amir;Dan Roth
Affiliations:
University of Illinois at Urbana-Champaign, Department of Computer Science, Urbana, IL;University of Illinois at Urbana-Champaign, Department of Computer Science, Urbana, IL;University of Illinois at Urbana-Champaign, Department of Computer Science, Urbana, IL
Venue:
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Year:
2005

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Abstract

Most probabilistic inference algorithms are specified and processed on a propositional level. In the last decade, many proposals for algorithms accepting first-order specifications have been presented, but in the inference stage they still operate on a mostly propositional representation level. [Poole, 2003] presented a method to perform inference directly on the first-order level, but this method is limited to special cases. In this paperwe present the first exact inference algorithm that operates directly on a first-order level, and that can be applied to any first-order model (specified in a language that generalizes undirected graphical models). Our experiments show superior performance in comparison with propositional exact inference.