7 citations · 19 across the 17 of their papers we have counts for
5 papers · 1 filter
Parallel Constraint-Driven Inductive Logic Programming
Andrew Cropper, Oghenejokpeme Orhobor, Cristian Dinu +1
Multi-core machines are ubiquitous. However, most inductive logic programming (ILP) approaches use only a single core, which severely limits their scalability. To address this limi…
Learning logic programs through divide, constrain, and conquer
Andrew Cropper
We introduce an inductive logic programming approach that combines classical divide-and-conquer search with modern constraint-driven search. Our anytime approach can learn optimal,…
Predicate Invention by Learning From Failures
Andrew Cropper, Rolf Morel
Discovering novel high-level concepts is one of the most important steps needed for human-level AI. In inductive logic programming (ILP), discovering novel high-level concepts is k…
Learning logic programs by explaining their failures
Rolf Morel, Andrew Cropper
Scientists form hypotheses and experimentally test them. If a hypothesis fails (is refuted), scientists try to explain the failure to eliminate other hypotheses. The more precise t…
Inductive logic programming at 30
Andrew Cropper, Sebastijan Dumančić, Richard Evans +1
Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples. As ILP t…