Constraint Acquisition via Partial Queries
Abstract
We learn constraint networks by asking the user partial queries. That is, we ask the user to classify assignments to subsets of the variables as positive or negative. We provide an algorithm that, given a negative example, focuses onto a constraint of the target network in a number of queries logarithmic in the size of the example. We give information theoretic lower bounds for learning some simple classes of constraint networks and show that our generic algorithm is optimal in some cases. Finally we evaluate our algorithm on some benchmarks.
Cite
Text
Bessiere et al. "Constraint Acquisition via Partial Queries." International Joint Conference on Artificial Intelligence, 2013.Markdown
[Bessiere et al. "Constraint Acquisition via Partial Queries." International Joint Conference on Artificial Intelligence, 2013.](https://mlanthology.org/ijcai/2013/bessiere2013ijcai-constraint/)BibTeX
@inproceedings{bessiere2013ijcai-constraint,
title = {{Constraint Acquisition via Partial Queries}},
author = {Bessiere, Christian and Coletta, Remi and Hebrard, Emmanuel and Katsirelos, George and Lazaar, Nadjib and Narodytska, Nina and Quimper, Claude-Guy and Walsh, Toby},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2013},
pages = {475-481},
url = {https://mlanthology.org/ijcai/2013/bessiere2013ijcai-constraint/}
}