Results 271 to 280 of about 224,550 (312)

Probabilistic Logic Programming in Action

2017
Probabilistic Programming (PP) has recently emerged as an effective approach for building complex probabilistic models. Until recently PP was mostly focused on functional programming while now Probabilistic Logic Programming (PLP) forms a significant subfield.
Arnaud, Nguembang Fadja, Riguzzi, Fabrizio   +1 more
openaire   +2 more sources

A survey of probabilistic logic programming

Declarative Logic Programming, 2018
The combination of logic programming and probability has proven useful for modeling domains with complex and uncertain relationships among elements. Many probabilistic logic programming (PLP) semantics have been proposed; among these, the distribution ...
Fabrizio Riguzzi, T. Swift
semanticscholar   +1 more source

The Event Calculus in Probabilistic Logic Programming with Annotated Disjunctions

Adaptive Agents and Multi-Agent Systems, 2017
We propose a new probabilistic extension to the event calculus using the probabilistic logic programming (PLP) language ProbLog, and a language construct called the annotated disjunction.
Kevin McAreavey, Kim Bauters, Weiru Liu, Jun Hong   +3 more
semanticscholar   +1 more source

Quantum probabilistic logic programming

SPIE Proceedings, 2015
We describe a quantum mechanics based logic programming language that supports Horn clauses, random variables, and covariance matrices to express and solve problems in probabilistic logic. The Horn clauses of the language wrap random variables, including infinite valued, to express probability distributions and statistical correlations, a powerful ...
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Probabilistic Functional Logic Programming

2017
This paper presents PFLP, a library for probabilistic programming in the functional logic programming language Curry. It demonstrates how the concepts of a functional logic programming language support the implementation of a library for probabilistic programming.
Sandra Dylus, Jan Christiansen, Finn Teegen   +2 more
openaire   +1 more source

Abduction in Probabilistic Logic Programs

2022
The representation of scenarios drawn from real-world domains is certainly favored by the presence of simple but powerful languages capable of capturing all facets of the problem. Probabilistic Logic Programming (PLP) [5,11] plays a fundamental role in this thanks to its ability to represent uncertain and complex information [3,10] and the possibility ...
Azzolini D., Bellodi E., Ferilli S., Riguzzi F., Zese R.   +4 more
openaire   +1 more source

Probabilistic Logic Programming (PLP 2017)

International Journal of Approximate Reasoning, 2019
Probabilistic Logic Programming (PLP) has come to the fore in the last decades as one of the most promising ways to model and reason on many different domains, including for example bioinformatics, semantic web, robotics, and computer vision. Such domains have in common the fact that information may be incomplete and/or uncertain, requiring approaches ...
Christian Theil Have, Riccardo Zese
openaire   +2 more sources

Abduction with probabilistic logic programming under the distribution semantics

International Journal of Approximate Reasoning, 2021
Damiano Azzolini, Elena Bellodi, S. Ferilli, Fabrizio Riguzzi, Riccardo Zese   +4 more
semanticscholar   +1 more source

Probabilistic Inductive Logic Programming on the Web

2017
Probabilistic Inductive Logic Programming (PILP) is gaining attention for its capability of modeling complex domains containing uncertain relationships among entities. Among PILP systems, cplint provides inference and learning algorithms competitive with the state of the art. Besides parameter learning, cplint provides one of the few structure learning
RIGUZZI, Fabrizio, ZESE, Riccardo, COTA, Giuseppe   +2 more
openaire   +1 more source

Learning the Structure of Probabilistic Logic Programs

2012
There is a growing interest in the field of Probabilistic Inductive Logic Programming, which uses languages that integrate logic programming and probability. Many of these languages are based on the distribution semantics and recently various authors have proposed systems for learning the parameters (PRISM, LeProbLog, LFI-ProbLog and EMBLEM) or both ...
BELLODI, Elena, RIGUZZI, Fabrizio
openaire   +1 more source