Lazy Explanation-Based Approximation for Probabilistic Logic Programming
Artificial Intelligence
2015-07-13 v1
Abstract
We introduce a lazy approach to the explanation-based approximation of probabilistic logic programs. It uses only the most significant part of the program when searching for explanations. The result is a fast and anytime approximate inference algorithm which returns hard lower and upper bounds on the exact probability. We experimentally show that this method outperforms state-of-the-art approximate inference.
Cite
@article{arxiv.1507.02873,
title = {Lazy Explanation-Based Approximation for Probabilistic Logic Programming},
author = {Joris Renkens and Angelika Kimmig and Luc De Raedt},
journal= {arXiv preprint arXiv:1507.02873},
year = {2015}
}