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Exploiting Answer Set Programming with External Sources for Meta-Interpretive Learning
2018
Theory and Practice of Logic Programming
AbstractMeta-Interpretive Learning (MIL) learns logic programs from examples by instantiating meta-rules, which is implemented by the Metagol system based on Prolog. Viewing MIL-problems as combinatorial search problems, they can alternatively be solved by employing Answer Set Programming (ASP), which may result in performance gains as a result of efficient conflict propagation. However, a straightforward ASP-encoding of MIL results in a huge search space due to a lack of procedural bias and
doi:10.1017/s1471068418000261
fatcat:25lneqkl4jgbhlkvzofvzyjo6i