ITEM METADATA RECORD
Title: Compiling probabilistic logic programs into sentential decision diagrams
Authors: Vlasselaer, Jonas ×
Renkens, Joris
Van den Broeck, Guy
De Raedt, Luc #
Issue Date: Jul-2014
Host Document: Proceedings Workshop on Probabilistic Logic Programming (PLP) pages:1-10
Conference: Workshop on Probabilistic Logic Programming (PLP) location:Vienna date:17 July 2014
Article number: 3
Abstract: Knowledge compilation algorithms transform a probabilistic
logic program into a circuit representation that permits efficient proba-
bility computation. Knowledge compilation underlies algorithms for ex-
act probabilistic inference and parameter learning in several languages,
including ProbLog, PRISM, and LPADs. Developing such algorithms
involves a choice, of which circuit language to target, and which compi-
lation algorithm to use. Historically, Binary Decision Diagrams (BDDs)
have been a popular target language, whereas recently, deterministic-
Decomposable Negation Normal Form (d-DNNF) circuits were shown
to outperform BDDs on these tasks. We investigate the use of a new
language, called Sentential Decision Diagrams (SDDs), for inference in
probabilistic logic programs. SDDs combine desirable properties of BDDs
and d-DNNFs. Like BDDs, they support bottom-up compilation and cir-
cuit minimization, yet they are a more general and flexible representa-
tion. Our preliminary experiments show that compilation to SDD yields
smaller circuits and more scalable inference, outperforming the state of
the art in ProbLog inference.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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