Finite Domain Constraint Solver Learning

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.

• Type: Conference
• Year: 2003
• Where: IJCAI
• Authors: Arnaud Lallouet, Thi-Bich-Hanh Dao, Andrei Legtchenko, AbdelAli Ed-Dbali
• Keywords: Abstract Framework, Domain Constraint Solver, IJCAI 2003, IJCAI 2007, Learning Process