Efficient Explanations for Knowledge Compilation Languages
Artificial Intelligence
2021-07-09 v2
Abstract
Knowledge compilation (KC) languages find a growing number of practical uses, including in Constraint Programming (CP) and in Machine Learning (ML). In most applications, one natural question is how to explain the decisions made by models represented by a KC language. This paper shows that for many of the best known KC languages, well-known classes of explanations can be computed in polynomial time. These classes include deterministic decomposable negation normal form (d-DNNF), and so any KC language that is strictly less succinct than d-DNNF. Furthermore, the paper also investigates the conditions under which polynomial time computation of explanations can be extended to KC languages more succinct than d-DNNF.
Cite
@article{arxiv.2107.01654,
title = {Efficient Explanations for Knowledge Compilation Languages},
author = {Xuanxiang Huang and Yacine Izza and Alexey Ignatiev and Martin C. Cooper and Nicholas Asher and Joao Marques-Silva},
journal= {arXiv preprint arXiv:2107.01654},
year = {2021}
}