Query-Driven Constraint Acquisition
Abstract
The modelling and reformulation of constraint networks are recognised as important problems. The task of automatically acquiring a constraint network formulation of a problem from a subset of its solutions and non-solutions has been presented in the literature. However, the choice of such a subset was assumed to be made independently of the acquisition process. We present an approach in which an interactive acquisition system actively selects a good set of examples. We show that the number of examples required to acquire a constraint network is significantly reduced using our approach.
Cite
Text
Bessiere et al. "Query-Driven Constraint Acquisition." International Joint Conference on Artificial Intelligence, 2007.Markdown
[Bessiere et al. "Query-Driven Constraint Acquisition." International Joint Conference on Artificial Intelligence, 2007.](https://mlanthology.org/ijcai/2007/bessiere2007ijcai-query/)BibTeX
@inproceedings{bessiere2007ijcai-query,
title = {{Query-Driven Constraint Acquisition}},
author = {Bessiere, Christian and Coletta, Remi and O'Sullivan, Barry and Paulin, Mathias},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2007},
pages = {50-55},
url = {https://mlanthology.org/ijcai/2007/bessiere2007ijcai-query/}
}