Automatic Construction of Constraint Propagators

The design of a constraint solver is certainly a complex task involving the combination of good search algorithms and efficient constraint propagation. The behavior of propagation functions for n-ary constraints rely usually on specialized graph algorithms which depend on the nature of the constraint and the level of consistency to be enforced. The design of an efficient algorithm usually requires a lot of efforts from the developer. This has motivated a line of work which aims at automatically finding constraint propagators by using Machine Learning techniques. This only requires a specification of the constraint expressed either by a table or by a (logic) program. The advantages range from fast prototyping of global constraints to efficient handling of table constraints. These latter constraints are usually very irregular and never reused: giving them a specialized propagator would not be worth the effort. Usually, automatically constructed propagators do not enforce a consistency as strong as specifically tailored algorithms but the difference tend to reduce and the efforts consented to build the propagator are by far much lower. After recalling the properties of propagators, we present a survey of some existing approaches which aim to build automatically such functions.