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.
CITATION STYLE
Dao, T. B. H., Lallouet, A., Legtchenko, A., & Martin, L. (2002). Indexical-based solver learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2470, pp. 541–556). Springer Verlag. https://doi.org/10.1007/3-540-46135-3_36
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