Max Robotics — Global Service Robot Directory + US Certification

About

Andrew Cropper is a leading researcher in Inductive Logic Programming (ILP) and program synthesis, whose work fundamentally redefines how machines learn algorithms from data. His central focus is on learning efficient, interpretable programs—a pursuit that bridges the gap between machine learning and classical computer science. A key contribution is his pioneering work on learning higher-order logic programs through abstraction and invention (2016, 41 citations), which enables AI systems to automatically discover reusable program structures. Cropper’s research critically addresses the problem of program efficiency, introducing techniques to learn efficient logic programs (2018, 33 citations) that can distinguish between algorithms like permutation sort and merge sort based on computational complexity. His thesis, "Efficiently learning efficient programs" (2017, 18 citations), formalizes the dual challenge of learning programs quickly while ensuring the learned programs are themselves efficient. More recently, his "learning from failures" paradigm (2021) introduces a novel generate-test-constrain approach that dramatically improves the scalability of ILP systems. With over 130 citations across his most influential works, Cropper’s innovations are shaping the future of automated program discovery, with direct applications in robotics, game-playing AI, and automated software engineering.