# Probabilistic Inductive Logic Programming

@inproceedings{Raedt2004ProbabilisticIL, title={Probabilistic Inductive Logic Programming}, author={Luc De Raedt and Kristian Kersting}, booktitle={Probabilistic Inductive Logic Programming}, year={2004} }

Probabilistic inductive logic programming aka. statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety of different formalisms and learning techniques have been developed. A unifying characterization of the underlying learning settings, however, is missing so far.
In this chapter, we start from inductive logic…

## 387 Citations

Logic, Probability and Learning, or an Introduction to Statistical Relational Learning

- Computer ScienceSBIA
- 2008

This tutorial starts from classical settings for logic learning namely learning from entailment, learning from interpretations, and learning from proofs, and shows how they can be extended with probabilistic methods.

Probabilistic Logic Learning - A Tutorial Abstract

- Computer ScienceICLP
- 2009

This tutorial starts from classical settings for logic learning namely learning from entailment, learning from interpretations, and learning from proofs, and shows how they can be extended with probabilistic methods.

A History of Probabilistic Inductive Logic Programming

- Computer ScienceFront. Robot. AI
- 2014

An overview of PILP is presented and the main results are discussed, showing how structure learning systems use parameter learning as a subroutine to improve the quality of their results.

Statistical Relational Artificial Intelligence: Logic, Probability, and Computation

- Computer ScienceStatistical Relational Artificial Intelligence: Logic, Probability, and Computation
- 2016

This book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extensions of Bayesian networks.

Learning the Parameters of Probabilistic Logic Programs from Interpretations

- Computer ScienceECML/PKDD
- 2011

A novel parameter estimation algorithm LFI-ProbLog for learning ProbLog programs from partial interpretations, essentially a Soft-EM algorithm that constructs a propositional logic formula for each interpretation that is used to estimate the marginals of the probabilistic parameters.

Probabilistic Relational Learning and Inductive Logic Programming at a Global Scale

- Computer Science, PhilosophyILP
- 2010

This talk outlines how publishing ontologies, data, and probabilistic hypotheses/theories can let us base beliefs on evidence, and how the resulting world-wide mind can go beyond the aggregation of human knowledge.

Probabilistic Inductive Logic Programming Based on Answer Set Programming

- Computer ScienceArXiv
- 2014

We propose a new formal language for the expressive representation of probabilistic knowledge based on Answer Set Programming (ASP). It allows for the annotation of first-order formulas as well as…

On probabilistic inference in relational conditional logics

- Computer Science, PhilosophyLog. J. IGPL
- 2012

This work proposes two different semantics and model theories for interpreting first-order probabilistic conditional logic, addresses the problems of ambiguity that are raised by the difference between subjective and statistical views, and develops a comprehensive list of desirable properties for inductive model-based probabilism inference in relational frameworks.

Discriminative Learning with Markov Logic Networks

- Computer Science
- 2009

This proposal presents two new discriminative learning algorithms for Markov logic networks, one of which outperforms existing learning methods for MLNs and traditional ILP systems in terms of predictive accuracy, and its performance is comparable to state of theart results on some ILP benchmarks.

Inductive Logic Boosting

- Computer ScienceArXiv
- 2014

Inductive Logic Boosting framework is proposed to transform the relational dataset into a feature-based dataset, induces logic rules by boosting Problog Rule Trees and relaxes the independence constraint of pseudo-likelihood.

## References

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