Indexical-Based Solver Learning

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.