Learning Optimal Chain Graphs with Answer Set Programming
Abstract
Learning an optimal chain graph for a given probability distribution is an important but at the same time very hard computational problem. We present a new approach to solve this problem for various objective functions, and without making any assumption on the probability distribution at hand. Our approach is based on encoding the learning problem declaratively using the answer set programming (ASP) paradigm. Empirical results show that our approach provides at least as accurate solutions as the best solutions provided by the existing algorithms, and overall provides better accuracy than any single previous algorithm.
Cite
Text
Sonntag et al. "Learning Optimal Chain Graphs with Answer Set Programming." Conference on Uncertainty in Artificial Intelligence, 2015.Markdown
[Sonntag et al. "Learning Optimal Chain Graphs with Answer Set Programming." Conference on Uncertainty in Artificial Intelligence, 2015.](https://mlanthology.org/uai/2015/sonntag2015uai-learning/)BibTeX
@inproceedings{sonntag2015uai-learning,
title = {{Learning Optimal Chain Graphs with Answer Set Programming}},
author = {Sonntag, Dag and Järvisalo, Matti and Peña, José M. and Hyttinen, Antti},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2015},
pages = {822-831},
url = {https://mlanthology.org/uai/2015/sonntag2015uai-learning/}
}