Semirings for probabilistic and neuro-symbolic logic programming

The field of probabilistic logic programming (PLP) focuses on integrating probabilistic models into programming languages based on logic. Over the past 30 years, numerous languages and frameworks have been developed for modeling, inference and learning in probabilistic logic programs. While original...

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Published in:International journal of approximate reasoning Vol. 171; p. 109130
Main Authors: Derkinderen, Vincent, Manhaeve, Robin, Zuidberg Dos Martires, Pedro, De Raedt, Luc
Format: Journal Article
Language:English
Published: Elsevier Inc 01.08.2024
Subjects:
Model counting
Neuro-symbolic AI
Probabilistic logic programming
Semiring programming
Semiring programming
Model counting
Neuro-symbolic AI
Probabilistic logic programming
ISSN:0888-613X, 1873-4731, 1873-4731
Online Access:Get full text
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Abstract The field of probabilistic logic programming (PLP) focuses on integrating probabilistic models into programming languages based on logic. Over the past 30 years, numerous languages and frameworks have been developed for modeling, inference and learning in probabilistic logic programs. While originally PLP focused on discrete probability, more recent approaches have incorporated continuous distributions as well as neural networks, effectively yielding neuro-symbolic methods. We provide an overview and synthesis of this domain, thereby contributing a unified algebraic perspective on the different flavors of PLP, showing that many if not most of the extensions of PLP can be cast within a common algebraic logic programming framework, in which facts are labeled with elements of a semiring and disjunction and conjunction are replaced by addition and multiplication. This does not only hold for the PLP variations itself but also for the underlying execution mechanism that is based on (algebraic) model counting. In order to showcase and explain this unified perspective, we focus on the ProbLog language and its extensions.
AbstractList The field of probabilistic logic programming (PLP) focuses on integrating probabilistic models into programming languages based on logic. Over the past 30 years, numerous languages and frameworks have been developed for modeling, inference and learning in probabilistic logic programs. While originally PLP focused on discrete probability, more recent approaches have incorporated continuous distributions as well as neural networks, effectively yielding neuro-symbolic methods. We provide an overview and synthesis of this domain, thereby contributing a unified algebraic perspective on the different flavors of PLP, showing that many if not most of the extensions of PLP can be cast within a common algebraic logic programming framework, in which facts are labeled with elements of a semiring and disjunction and conjunction are replaced by addition and multiplication. This does not only hold for the PLP variations itself but also for the underlying execution mechanism that is based on (algebraic) model counting. In order to showcase and explain this unified perspective, we focus on the ProbLog language and its extensions.
The field of probabilistic logic programming (PLP) focuses on integrating probabilistic models into programming languages based on logic. Over the past 30 years, numerous languages and frameworks have been developed for modeling, inference and learning in probabilistic logic programs. While originally PLP focused on discrete probability, more recent approaches have incorporated continuous distributions as well as neural networks, effectively yielding neurosymbolic methods. We provide an overview and synthesis of this domain, thereby contributing a unified algebraic perspective on the different flavors of PLP, showing that many if not most of the extensions of PLP can be cast within a common algebraic logic programming framework, in which facts are labeled with elements of a semiring and disjunction and conjunction are replaced by addition and multiplication. This does not only hold for the PLP variations itself but also for the underlying execution mechanism that is based on (algebraic) model counting. In order to showcase and explain this unified perspective, we focus on the ProbLog language and its extensions.
ArticleNumber 109130
Author Zuidberg Dos Martires, Pedro
Manhaeve, Robin
De Raedt, Luc
Derkinderen, Vincent
Author_xml – sequence: 1
  givenname: Vincent
  orcidid: 0000-0002-8894-270X
  surname: Derkinderen
  fullname: Derkinderen, Vincent
  email: vincent.derkinderen@kuleuven.be
  organization: DTAI, Dept. of Computer Science, KU Leuven, Leuven, B-3000, Belgium
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  givenname: Robin
  surname: Manhaeve
  fullname: Manhaeve, Robin
  email: robin.manhaeve@kuleuven.be
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  givenname: Pedro
  surname: Zuidberg Dos Martires
  fullname: Zuidberg Dos Martires, Pedro
  email: pedro.zuidberg-dos-martires@oru.se
  organization: Center for Applied Autonomous Systems, Örebro University, Örebro, SE-70182, Sweden
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  givenname: Luc
  surname: De Raedt
  fullname: De Raedt, Luc
  email: luc.deraedt@kuleuven.be
  organization: DTAI, Dept. of Computer Science, KU Leuven, Leuven, B-3000, Belgium
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Keywords Semiring programming
Model counting
Neuro-symbolic AI
Probabilistic logic programming
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Snippet The field of probabilistic logic programming (PLP) focuses on integrating probabilistic models into programming languages based on logic. Over the past 30...
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SubjectTerms Model counting
Neuro-symbolic AI
Probabilistic logic programming
Semiring programming
Title Semirings for probabilistic and neuro-symbolic logic programming
URI https://dx.doi.org/10.1016/j.ijar.2024.109130
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