Title
Learning Approximate Consistencies
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 present sufficient conditions for the learned solver to be correct and complete with respect to the original constraint. We instantiate this framework to the learning of bound-consistency in the indexical language of Gnu-Prolog.
Year
DOI
Venue
2003
10.1007/978-3-540-24662-6_5
Lecture Notes in Computer Science
Keywords
Field
DocType
indexation
Indexicality,Constraint programming,Theoretical computer science,Constraint satisfaction problem,Operator (computer programming),Solver,Mathematics
Conference
Volume
ISSN
Citations 
3010
0302-9743
0
PageRank 
References 
Authors
0.34
19
4
Name
Order
Citations
PageRank
Arnaud Lallouet19915.64
Andrei Legtchenko2284.46
Thi-bich-hanh Dao39313.48
Abdelali Ed-dbali4103.71