Learning MDL Logic Programs from Noisy Data

Many inductive logic programming approaches struggle to learn programs from noisy data. To overcome this limitation, we introduce an approach that learns minimal description length programs from noisy data, including recursive programs. Our experiments on several domains, including drug design, game playing, and program synthesis, show that our approach can outperform existing approaches in terms of predictive accuracies and scale to moderate amounts of noise. Introduction The goal of inductive logic programming (ILP) (Muggleton 1991) is to induce a logic program (a set of logical rules) that generalises training examples and background knowledge.