A Fully Connectionist Model Generator for Covered First-Order Logic Programs
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 666-670.
Abstract
We present a fully connectionist system for the learning of first-order logic programs and the generation of corresponding models: Given a program and a set of training examples, we embed the associated semantic operator into a feed-forward network and train the network using the examples. This results in the learning of first-order knowledge while damaged or noisy data is handled gracefully.
Sebastian Bader, Pascal Hitzler, Steffen Hölldobler, and Andreas Witzel. 2007. A Fully Connectionist Model Generator for Covered First-Order Logic Programs. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07. IJCAI, 666–670. https://ijcai.org/proceedings/2007/106
Bader, S., Hitzler, P., Hölldobler, S., & Witzel, A. (2007). A Fully Connectionist Model Generator for Covered First-Order Logic Programs. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 (pp. 666–670). IJCAI. https://ijcai.org/proceedings/2007/106
@inproceedings{ijcai2007p106,
title = {A Fully Connectionist Model Generator for Covered First-Order Logic Programs},
author = {Bader, Sebastian and Hitzler, Pascal and Hölldobler, Steffen and Witzel, Andreas},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {666--670},
year = {2007},
url = {https://ijcai.org/proceedings/2007/106},
}
TY - CPAPER TI - A Fully Connectionist Model Generator for Covered First-Order Logic Programs AU - Bader, Sebastian AU - Hitzler, Pascal AU - Hölldobler, Steffen AU - Witzel, Andreas T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 666 EP - 670 UR - https://ijcai.org/proceedings/2007/106 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: