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Declarative probabilistic logic programming in discrete-continuous domains

delete2024-12-01
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P
Pedro Zuidberg Dos Martires *
L
Luc De Raedt
A
Angelika Kimmig
DOI:10.1016/j.artint.2024.104227delete
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摘要

摘要

En 中文
Over the past three decades, the logic programming paradigm has been successfully expanded to support probabilistic modeling, inference and learning. The resulting paradigm of probabilistic logic programming (PLP) and its programming languages owes much of its success to a declarative semantics, the so-called distribution semantics. However, the distribution semantics is limited to discrete random variables only. While PLP has been extended in various ways for supporting hybrid, that is, mixed discrete and continuous random variables, we are still lacking a declarative semantics for hybrid PLP that not only generalizes the distribution semantics and the modeling language but also the standard inference algorithm that is based on knowledge compilation. We contribute the measure semantics together with the hybrid PLP language DC-ProbLog (where DC stands for distributional clauses) and its inference engine infinitesimal algebraic likelihood weighting (IALW). These have the original distribution semantics, standard PLP languages such as ProbLog, and standard inference engines for PLP based on knowledge compilation as special cases. Thus, we generalize the state of the art of PLP towards hybrid PLP in three different aspects: semantics, language and inference. Furthermore, IALW is the first inference algorithm for hybrid probabilistic programming based on knowledge compilation.
Keyword:
Probabilistic programming
Declarative semantics
Discrete-continuous distributions
Likelihood weighting
Logic programming
Knowledge compilation
Algebraic model counting
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期刊

Artificial Intelligence Review 封面图
Artificial Intelligence Review
IF:
13.9
论文数:
6.1K
被引数:
1.9W

机构

K
KU Leuven
学者数:
5.7W
论文数: 5.2W
被引数: 8.1W
O
Orebro University
学者数:
5.0K
论文数: 4.7K
被引数: 51
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