Abstract
The pioneering works of Apt and Monfroy, and Abdennadher and Rigotti have shown that the construction of rule-based solvers can be automated using machine learning techniques. Both works implement the solver as a set of CHRs. But many solvers use the more specialized chaotic iteration of operators as operational semantics and not CHR’s rewriting semantics. In this paper, we first define a language-independent framework for operator learning and then we apply it to the learning of partial arc-consistency operators for a subset of the indexical language of Gnu-Prolog and show the effectiveness of our approach by two implementations. On tested examples, Gnu-Prolog solvers are learned from their original constraints and powerful propagators are found for user-defined constraints.
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Dao, T.B.H., Lallouet, A., Legtchenko, A., Martin, L. (2002). Indexical-Based Solver Learning. In: Van Hentenryck, P. (eds) Principles and Practice of Constraint Programming - CP 2002. CP 2002. Lecture Notes in Computer Science, vol 2470. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46135-3_36
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DOI: https://doi.org/10.1007/3-540-46135-3_36
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