Add to ORKGIn Datalog, missing values are represented by Skolem constants. More generally, in logic programming missing values, or existentiallyquantified variables, are represented by terms built from Skolem functors. In an analogy to probabilistic relational models (PRMs), we wish to represent the joint probability distribution over missing values in a database or logic program using a Bayesian network. This paper presents an extension of logic programs that makes it possible to specify a joint probability distribution over terms built from Skolem functors in the program. Our extension is based on constraint logic programming (CLP), so we call the extended language CLP(£¥ ¤). We show that CLP(£¥ ¤) subsumes PRMs; this greater expressivity carries bo...
Probabilistic programming is an emerging subfield of AI that extends traditional programming languag...
Probabilistic Logic Programming (PLP) has come to the fore in the last decades as one of the most pr...
A significant part of current research on (inductive) logic programming deals with probabilistic log...
In Datalog, missing values are represented by Skolem constants. More generally, in logic programmi...
In Datalog, missing values are represented by Skolem constants. More generally, in logic programming...
We present CLP(BN), a novel approach that aims at expressing Bayesian networks through the constrain...
We present CLP(BN), a novel approach that aims at expressing Bayesian networks through the constrain...
. This paper addresses two central problems for probabilistic processing models: parameter estimatio...
We propose a declarative-based implementation of randomised algorithms, which exploits the Constrain...
Probabilistic logics combine the expressive power of logic with the ability to reason with uncertain...
Abstract Probabilistic logics combine the expressive power of logic with the ability to reason with ...
Probabilistic logics combine the expressive power of logic with the ability to reason with uncertain...
Probabilistic logics combine the expressive power of logic with the ability to reason with uncertain...
Most approaches to probabilistic logic programming deal with deduction systems and xpoint semantics ...
The combination of logic programming and probability has proven useful for modeling domains with com...
Probabilistic programming is an emerging subfield of AI that extends traditional programming languag...
Probabilistic Logic Programming (PLP) has come to the fore in the last decades as one of the most pr...
A significant part of current research on (inductive) logic programming deals with probabilistic log...
In Datalog, missing values are represented by Skolem constants. More generally, in logic programmi...
In Datalog, missing values are represented by Skolem constants. More generally, in logic programming...
We present CLP(BN), a novel approach that aims at expressing Bayesian networks through the constrain...
We present CLP(BN), a novel approach that aims at expressing Bayesian networks through the constrain...
. This paper addresses two central problems for probabilistic processing models: parameter estimatio...
We propose a declarative-based implementation of randomised algorithms, which exploits the Constrain...
Probabilistic logics combine the expressive power of logic with the ability to reason with uncertain...
Abstract Probabilistic logics combine the expressive power of logic with the ability to reason with ...
Probabilistic logics combine the expressive power of logic with the ability to reason with uncertain...
Probabilistic logics combine the expressive power of logic with the ability to reason with uncertain...
Most approaches to probabilistic logic programming deal with deduction systems and xpoint semantics ...
The combination of logic programming and probability has proven useful for modeling domains with com...
Probabilistic programming is an emerging subfield of AI that extends traditional programming languag...
Probabilistic Logic Programming (PLP) has come to the fore in the last decades as one of the most pr...
A significant part of current research on (inductive) logic programming deals with probabilistic log...