Indexical-Based Solver Learning

  • Authors:
  • Thi-Bich-Hanh Dao;Arnaud Lallouet;Andrei Legtchenko;Lionel Martin

  • Affiliations:
  • -;-;-;-

  • Venue:
  • CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
  • Year:
  • 2002

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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.