Declarative probabilistic logic programming in discrete-continuous domains

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...

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Published in:Artificial intelligence Vol. 337; p. 104227
Main Authors: Zuidberg Dos Martires, Pedro, De Raedt, Luc, Kimmig, Angelika
Format: Journal Article
Language:English
Published: Elsevier B.V 01.12.2024
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ISSN:0004-3702, 1872-7921
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Abstract 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.
AbstractList 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.
ArticleNumber 104227
Author Zuidberg Dos Martires, Pedro
De Raedt, Luc
Kimmig, Angelika
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  surname: Zuidberg Dos Martires
  fullname: Zuidberg Dos Martires, Pedro
  email: pedro.zuidberg-dos-martires@oru.se
  organization: Centre for Applied Autonomous Sensor Systems, Örebro University, Sweden
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  givenname: Luc
  surname: De Raedt
  fullname: De Raedt, Luc
  organization: Centre for Applied Autonomous Sensor Systems, Örebro University, Sweden
– sequence: 3
  givenname: Angelika
  surname: Kimmig
  fullname: Kimmig, Angelika
  organization: Department of Computer Science, KU Leuven, Belgium
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Keywords Logic programming
Probabilistic programming
Discrete-continuous distributions
Algebraic model counting
Declarative semantics
Likelihood weighting
Knowledge compilation
Language English
License This is an open access article under the CC BY license.
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Snippet Over the past three decades, the logic programming paradigm has been successfully expanded to support probabilistic modeling, inference and learning. The...
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StartPage 104227
SubjectTerms Algebraic model counting
Declarative semantics
Discrete-continuous distributions
Knowledge compilation
Likelihood weighting
Logic programming
Probabilistic programming
Title Declarative probabilistic logic programming in discrete-continuous domains
URI https://dx.doi.org/10.1016/j.artint.2024.104227
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