Constraint-Based Inference in Probabilistic Logic Programs
Probabilistic Logic Programs (PLPs) generalize traditional logic programs and allow the encoding of models combining logical structure and uncertainty. In PLP, inference is performed by summarizing the possible worlds which entail the query in a suitable data structure, and using it to compute the a...
Saved in:
Main Authors | , , |
---|---|
Format | Journal Article |
Language | English |
Published |
26.04.2018
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Probabilistic Logic Programs (PLPs) generalize traditional logic programs and
allow the encoding of models combining logical structure and uncertainty. In
PLP, inference is performed by summarizing the possible worlds which entail the
query in a suitable data structure, and using it to compute the answer
probability. Systems such as ProbLog, PITA, etc., use propositional data
structures like explanation graphs, BDDs, SDDs, etc., to represent the possible
worlds. While this approach saves inference time due to substructure sharing,
there are a number of problems where a more compact data structure is possible.
We propose a data structure called Ordered Symbolic Derivation Diagram (OSDD)
which captures the possible worlds by means of constraint formulas. We describe
a program transformation technique to construct OSDDs via query evaluation, and
give procedures to perform exact and approximate inference over OSDDs. Our
approach has two key properties. Firstly, the exact inference procedure is a
generalization of traditional inference, and results in speedup over the latter
in certain settings. Secondly, the approximate technique is a generalization of
likelihood weighting in Bayesian Networks, and allows us to perform
sampling-based inference with lower rejection rate and variance. We evaluate
the effectiveness of the proposed techniques through experiments on several
problems. This paper is under consideration for acceptance in TPLP. |
---|---|
DOI: | 10.48550/arxiv.1804.10237 |