Learning from Entailment of Logic Programs with Local Variables
Abstract
In this paper, we study exact learning of logic programs from entailment and present a polynomial time algorithm to learn a rich class of logic programs that allow local variables and include many standard programs like append, merge, split, delete, member, prefix, suffix, length, reverse, append/4 on lists, tree traversal programs on binary trees and addition, multiplication, exponentiation on natural numbers. Grafting a few aspects of incremental learning [ 9 ] onto the framework of learning from entailment [ 3 ], we generalize the existing results to allow local variables, which play an important role of sideways information passing in the paradigm of logic programming.
Cite
Text
Rao and Sattar. "Learning from Entailment of Logic Programs with Local Variables." International Conference on Algorithmic Learning Theory, 1998. doi:10.1007/3-540-49730-7_11Markdown
[Rao and Sattar. "Learning from Entailment of Logic Programs with Local Variables." International Conference on Algorithmic Learning Theory, 1998.](https://mlanthology.org/alt/1998/rao1998alt-learning/) doi:10.1007/3-540-49730-7_11BibTeX
@inproceedings{rao1998alt-learning,
title = {{Learning from Entailment of Logic Programs with Local Variables}},
author = {Rao, M. R. K. Krishna and Sattar, Abdul},
booktitle = {International Conference on Algorithmic Learning Theory},
year = {1998},
pages = {143-157},
doi = {10.1007/3-540-49730-7_11},
url = {https://mlanthology.org/alt/1998/rao1998alt-learning/}
}