Abstract
We investigate the problem of learning constraint satisfaction problems from an inductive logic programming perspective. Constraint satisfaction problems are the underlying basis for constraint programming and there is a long standing interest in techniques for learning these. Constraint satisfaction problems are often described using a relational logic, so inductive logic programming is a natural candidate for learning such problems. So far, there is however only little work on the intersection between learning constraint satisfaction problems and inductive logic programming. In this article, we point out several similarities and differences between the two classes of techniques that may inspire further cross-fertilization between these two fields.
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Notes
- 1.
Observe that we choose here to represent global constraints as unary predicates, taking a list of variables as its arguments. An alternative would be to introduce one version of the global predicate for any possible arity, e.g. alldifferent(X, Y), alldifferent(X, Y, Z), ....
- 2.
It is well-known in ILP [12] that when learning from interpretations, a hypothesis G is more general than S if and only if \(S\, \models \, G\), while when learning from entailment if and only if \(G\, \models \, S\).
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This work was supported by the European Commission under the project “Inductive Constraint Programming” (FP7- 284715).
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De Raedt, L., Dries, A., Guns, T., Bessiere, C. (2016). Learning Constraint Satisfaction Problems: An ILP Perspective. In: Bessiere, C., De Raedt, L., Kotthoff, L., Nijssen, S., O'Sullivan, B., Pedreschi, D. (eds) Data Mining and Constraint Programming. Lecture Notes in Computer Science(), vol 10101. Springer, Cham. https://doi.org/10.1007/978-3-319-50137-6_5
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