Derivation reduction of metarules in meta-interpretive learning - Cropper, A; Tourret, S (Springer, 2018)
Meta-interpretive learning (MIL) is a form of inductive logic programming. MIL uses second-order Horn clauses, called metarules, as a form of declarative bias. Metarules define the structures of learnable programs and thus the hypothesis space. Deciding which metarules to use is a trade-off between efficiency and expressivity. The hypothesis space increases given more metarules, so we wish to use fewer metarules, but if we use too few metarules then we lose expressivity. A recent paper used Progol’s entailment reduction algorithm to identify irreducible, or minimal, sets of metarules. In some cases, as few as two metarules were shown to be sufficient to entail all hypotheses in an infinite language. Moreover, it was shown that compared to non-minimal sets, learning with minimal sets of metarules improves predictive accuracies and lowers learning times. In this paper, we show that entailment reduction can be too strong and can remove metarules necessary to make a hypothesis more specific. We describe a new reduction technique based on derivations. Specifically, we introduce the derivation reduction problem, the problem of finding a finite subset of a Horn theory from which the whole theory can be derived using SLD-resolution. We describe a derivation reduction algorithm which we use to reduce sets of metarules. We also theoretically study whether certain sets of metarules can be derivationally reduced to minimal finite subsets. Our experiments compare learning with entailment and derivation reduced sets of metarules. In general, using derivation reduced sets of metarules outperforms using entailment reduced sets of metarules, both in terms of predictive accuracies and learning times.