Probabilistic (logic) programming concepts

A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particu...

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Vydáno v:	Machine learning Ročník 100; číslo 1; s. 5 - 47
Hlavní autoři:	De Raedt, Luc, Kimmig, Angelika
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.07.2015 Springer Nature B.V
Témata:	Artificial Intelligence Computer Science Control Inference in probabilistic languages Logic programming Mechatronics Natural Language Processing (NLP) Probabilistic logic programming Probabilistic methods Probabilistic programming languages Probability theory Programming Programming languages Prolog Prolog (programming language) Robotics Semantics Simulation and Modeling Statistical relational learning Inference in probabilistic languages Statistical relational learning Probabilistic programming languages Probabilistic logic programming
ISSN:	0885-6125, 1573-0565, 1573-0565
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Abstract	A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying programming concepts and appreciate the differences between the different languages. To obtain a better understanding of probabilistic programming, we identify a number of core programming concepts underlying the primitives used by various probabilistic languages, discuss the execution mechanisms that they require and use these to position and survey state-of-the-art probabilistic languages and their implementation. While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been considered for over 20 years.
AbstractList	A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying programming concepts and appreciate the differences between the different languages. To obtain a better understanding of probabilistic programming, we identify a number of core programming concepts underlying the primitives used by various probabilistic languages, discuss the execution mechanisms that they require and use these to position and survey state-of-the-art probabilistic languages and their implementation. While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been considered for over 20 years. A multitude of different probabilistic programming languages exists today, allextending a traditional programming language with primitives to support modeling ofcomplex, structured probability distributions. Each of these languages employs its own prob-abilistic primitives, and comes with a particular syntax, semantics and inference procedure.This makes it hard to understand the underlying programming concepts and appreciate thedifferences between the different languages. To obtain a better understanding of probabilisticprogramming, we identify a number of core programming concepts underlying the primi-tives used by various probabilistic languages, discuss the execution mechanisms that theyrequire and use these to position and survey state-of-the-art probabilistic languages and theirimplementation. While doing so, we focus on probabilistic extensions oflogicprogramminglanguages such as Prolog, which have been considered for over 20 years. Issue Title: Special Issue on Inductive Logic Programming and on Multi-Relational Learning; Guest Editors: Gerson Zaverucha and Vitor Santos Costa A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying programming concepts and appreciate the differences between the different languages. To obtain a better understanding of probabilistic programming, we identify a number of core programming concepts underlying the primitives used by various probabilistic languages, discuss the execution mechanisms that they require and use these to position and survey state-of-the-art probabilistic languages and their implementation. While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been considered for over 20 years. A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying programming concepts and appreciate the differences between the different languages. To obtain a better understanding of probabilistic programming, we identify a number of core programming concepts underlying the primitives used by various probabilistic languages, discuss the execution mechanisms that they require and use these to position and survey state-of-the-art probabilistic languages and their implementation. While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been considered for over 20 years.
Author	De Raedt, Luc Kimmig, Angelika
Author_xml	– sequence: 1 givenname: Luc surname: De Raedt fullname: De Raedt, Luc organization: Department of Computer Science, KU Leuven – sequence: 2 givenname: Angelika surname: Kimmig fullname: Kimmig, Angelika email: angelika.kimmig@cs.kuleuven.be organization: Department of Computer Science, KU Leuven
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Snippet	A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling... Issue Title: Special Issue on Inductive Logic Programming and on Multi-Relational Learning; Guest Editors: Gerson Zaverucha and Vitor Santos Costa A multitude... A multitude of different probabilistic programming languages exists today, allextending a traditional programming language with primitives to support modeling...
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SubjectTerms	Artificial Intelligence Computer Science Control Inference in probabilistic languages Logic programming Mechatronics Natural Language Processing (NLP) Probabilistic logic programming Probabilistic methods Probabilistic programming languages Probability theory Programming Programming languages Prolog Prolog (programming language) Robotics Semantics Simulation and Modeling Statistical relational learning
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