Integrity Constraints and Interactive Concept-Learning

Abstract

We show how our interactive concept-learner Clint can be enhanced with capabilities to handle integrity constraints. In this version of Clint, the user may supply general first order logic clauses as integrity constraints to the system. The system will then assure that these constraints are satisfied by the learned knowledge base.

Cite

Text

De Raedt et al. "Integrity Constraints and Interactive Concept-Learning." International Conference on Machine Learning, 1991. doi:10.1016/B978-1-55860-200-7.50081-7

Markdown

[De Raedt et al. "Integrity Constraints and Interactive Concept-Learning." International Conference on Machine Learning, 1991.](https://mlanthology.org/icml/1991/raedt1991icml-integrity/) doi:10.1016/B978-1-55860-200-7.50081-7

BibTeX

@inproceedings{raedt1991icml-integrity,
  title     = {{Integrity Constraints and Interactive Concept-Learning}},
  author    = {De Raedt, Luc and Bruynooghe, Maurice and Martens, Bern},
  booktitle = {International Conference on Machine Learning},
  year      = {1991},
  pages     = {394-398},
  doi       = {10.1016/B978-1-55860-200-7.50081-7},
  url       = {https://mlanthology.org/icml/1991/raedt1991icml-integrity/}
}