Probabilistic Inductive Logic Programming
Abstract
Probabilistic inductive logic programming, sometimes also called statistical relational learning, addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with first order logic representations and machine learning. A rich variety of different formalisms and learning techniques have been developed. In the present paper, we start from inductive logic programming and sketch how it can be extended with probabilistic methods. More precisely, we outline three classical settings for inductive logic programming, namely learning from entailment , learning from interpretations , and learning from proofs or traces , and show how they can be used to learn different types of probabilistic representations.
Cite
Text
De Raedt and Kersting. "Probabilistic Inductive Logic Programming." International Conference on Algorithmic Learning Theory, 2004. doi:10.1007/978-3-540-30215-5_3Markdown
[De Raedt and Kersting. "Probabilistic Inductive Logic Programming." International Conference on Algorithmic Learning Theory, 2004.](https://mlanthology.org/alt/2004/raedt2004alt-probabilistic/) doi:10.1007/978-3-540-30215-5_3BibTeX
@inproceedings{raedt2004alt-probabilistic,
title = {{Probabilistic Inductive Logic Programming}},
author = {De Raedt, Luc and Kersting, Kristian},
booktitle = {International Conference on Algorithmic Learning Theory},
year = {2004},
pages = {19-36},
doi = {10.1007/978-3-540-30215-5_3},
url = {https://mlanthology.org/alt/2004/raedt2004alt-probabilistic/}
}