---
title: "Finite Domain Constraint Solver Learning"
authors:
  - "Arnaud Lallouet"
  - "Thi-Bich-Hanh Dao"
  - "Andrei Legtchenko"
  - "AbdelAli Ed-Dbali"
doi: ""
url: "https://ijcai.org/proceedings/2003/205"
pdf: "https://ijcai.org/proceedings/2003/0205.pdf"
venue: "Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence"
year: 2003
pages: "1379-1379"
track: ""
keywords:
---

# Finite Domain Constraint Solver Learning

Arnaud Lallouet, Thi-Bich-Hanh Dao, Andrei Legtchenko, AbdelAli Ed-Dbali

*Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence*, pages 1379-1379.

## Abstract

In this paper, we present an abstract framework for learning a finite domain constraint solver modeled by a set of operators enforcing a consistency. The behavior of the consistency to be learned is taken as the set of examples on which the learning process is applied. The best possible expression of this operator in a given language is then searched. We instantiate this framework to the learning of bound-consistency in the indexical language of Gnu-Prolog.