Flattening and Saturation: Two Representation Changes for Generalization
Machine Learning - Special issue on evaluating and changing representation
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Morphosyntactic Tagging of Slovene Using Progol
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Compile the Hypothesis Space: Do it Once, Use it Often
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
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We investigate using the Mercury language to implement and design ILP algorithms, presenting our own ILP system IMP. Mercury provides faster execution than Prolog. Since Mercury is a purely declarative language, run-time assertion of induced clauses is prohibited. Instead IMPuses a problem-specific interpreter of ground representations of induced clauses. The interpreter is used both for cover testing and bottom clause generation. The Mercury source for this interpreter is generated automatically from the user's background knowledge using Moose, a Mercury parser generator. Our results include some encouraging results on IMP's cover testing speed, but overall IMPis still generally a little slower than ALEPH.