[LIVRE][B] Foundations of Probabilistic Logic Programming: Languages, semantics, inference and learning
F Riguzzi - 2023 - taylorfrancis.com
Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of
activity, with many proposals for languages and algorithms for inference and learning. This …
activity, with many proposals for languages and algorithms for inference and learning. This …
On the implementation of the probabilistic logic programming language ProbLog
The past few years have seen a surge of interest in the field of probabilistic logic learning
and statistical relational learning. In this endeavor, many probabilistic logics have been …
and statistical relational learning. In this endeavor, many probabilistic logics have been …
Structure learning of probabilistic logic programs by searching the clause space
Learning probabilistic logic programming languages is receiving an increasing attention,
and systems are available for learning the parameters (PRISM, LeProbLog, LFI-ProbLog …
and systems are available for learning the parameters (PRISM, LeProbLog, LFI-ProbLog …
The PITA system: Tabling and answer subsumption for reasoning under uncertainty
F Riguzzi, T Swift - Theory and Practice of Logic Programming, 2011 - cambridge.org
Many real world domains require the representation of a measure of uncertainty. The most
common such representation is probability, and the combination of probability with logic …
common such representation is probability, and the combination of probability with logic …
The magic of logical inference in probabilistic programming
B Gutmann, I Thon, A Kimmig… - Theory and Practice of …, 2011 - cambridge.org
Today, there exist many different probabilistic programming languages as well as more
inference mechanisms for these languages. Still, most logic programming-based languages …
inference mechanisms for these languages. Still, most logic programming-based languages …
A survey of probabilistic logic programming
F Riguzzi, T Swift - Declarative Logic Programming: Theory, Systems …, 2018 - dl.acm.org
The combination of logic programming and probability has proven useful for modeling
domains with complex and uncertain relationships among elements. Many probabilistic logic …
domains with complex and uncertain relationships among elements. Many probabilistic logic …
Probabilistic description logics under the distribution semantics
Representing uncertain information is crucial for modeling real world domains. In this paper
we present a technique for the integration of probabilistic information in Description Logics …
we present a technique for the integration of probabilistic information in Description Logics …
Dyna: Extending datalog for modern AI
J Eisner, NW Filardo - International Datalog 2.0 Workshop, 2010 - Springer
Modern statistical AI systems are quite large and complex; this interferes with research,
development, and education. We point out that most of the computation involves database …
development, and education. We point out that most of the computation involves database …
Expectation Maximization over binary decision diagrams for probabilistic logic programs
Recently much work in Machine Learning has concentrated on using expressive
representation languages that combine aspects of logic and probability. A whole field has …
representation languages that combine aspects of logic and probability. A whole field has …
Extending ProbLog with continuous distributions
ProbLog is a recently introduced probabilistic extension of Prolog. The key contribution of
this paper is that we extend ProbLog with abilities to specify continuous distributions and …
this paper is that we extend ProbLog with abilities to specify continuous distributions and …