---
title: "Multiple Constraint Acquisition"
authors:
  - "Robin Arcangioli"
  - "Christian Bessière"
  - "Nadjib Lazaar"
doi: ""
url: "https://ijcai.org/proceedings/2016/105"
pdf: "https://ijcai.org/proceedings/2016/0105.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "698-703"
track: ""
keywords:
---

# Multiple Constraint Acquisition

Robin Arcangioli, Christian Bessière, Nadjib Lazaar

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 698-703.

## Abstract

QUACQ is a constraint acquisition system that assists a non-expert user to model her problem as a constraint network by classifying (partial) examples as positive or negative. For each negative example, QUACQ focuses onto a constraint of the target network. The drawback is that the user may need to answer a great number of such examples to learn all the constraints. In this paper, we provide a new approach that is able to learn a maximum number of constraints violated by a given negative example. Finally we give an experimental evaluation that shows that our approach improves on QUACQ.