Add to ORKGacceptance rate 28.8%We study the problem of inducing logic programs in a probabilistic setting, in which both the example descriptions and their classification can be proba- bilistic. The setting is incorporated in the proba- bilistic rule learner ProbFOIL + , which combines principles of the rule learner FOIL with ProbLog, a probabilistic Prolog. We illustrate the approach by applying it to the knowledge base of NELL, the Never-Ending Language Learner.status: publishe
We present ProbLog2, the state of the art implementation of the probabilistic programming language P...
In the past few years there has been a lot of work lying at the intersection of probability theory, ...
The past few years have seen a surge of interest in the field of probabilistic logic learning and ...
We study the problem of inducing logic programs in a probabilistic setting, in which both the exampl...
We study the problem of inducing logic programs in a probabilistic setting, in which both the exampl...
Traditionally, rule learners have learned deterministic rules from deterministic data, that is, the ...
One of the key challenges in artificial intelligence is the integration of machine learning, relatio...
One of the key challenges in artificial intelligence is the integration of machine learning, relatio...
A multitude of different probabilistic programming languages exists today, all extending a tradition...
The tutorial will provide a motivation for, an overview of and an introduction to the fields of stat...
This thesis deals with Statistical Relational Learning (SRL), a research area combining principles a...
ProbLog is a recently introduced probabilistic extension of the logic programming language Prolog, i...
Probabilistic logic learning (PLL), sometimes also called statistical relational learning, addresses...
Abstract Keynote PresentationRules represent knowledge about the world that can be used for reasonin...
The past few years have seen a surge of interest in the field of probabilistic logic learning and st...
We present ProbLog2, the state of the art implementation of the probabilistic programming language P...
In the past few years there has been a lot of work lying at the intersection of probability theory, ...
The past few years have seen a surge of interest in the field of probabilistic logic learning and ...
We study the problem of inducing logic programs in a probabilistic setting, in which both the exampl...
We study the problem of inducing logic programs in a probabilistic setting, in which both the exampl...
Traditionally, rule learners have learned deterministic rules from deterministic data, that is, the ...
One of the key challenges in artificial intelligence is the integration of machine learning, relatio...
One of the key challenges in artificial intelligence is the integration of machine learning, relatio...
A multitude of different probabilistic programming languages exists today, all extending a tradition...
The tutorial will provide a motivation for, an overview of and an introduction to the fields of stat...
This thesis deals with Statistical Relational Learning (SRL), a research area combining principles a...
ProbLog is a recently introduced probabilistic extension of the logic programming language Prolog, i...
Probabilistic logic learning (PLL), sometimes also called statistical relational learning, addresses...
Abstract Keynote PresentationRules represent knowledge about the world that can be used for reasonin...
The past few years have seen a surge of interest in the field of probabilistic logic learning and st...
We present ProbLog2, the state of the art implementation of the probabilistic programming language P...
In the past few years there has been a lot of work lying at the intersection of probability theory, ...
The past few years have seen a surge of interest in the field of probabilistic logic learning and ...