A Generalised Framework for Property-Driven Machine Learning

Neural networks have been shown to frequently fail to satisfy critical safety and correctness properties after training, highlighting the pressing need for training methods that incorporate such properties directly. While adversarial training can be used to improve robustness to small perturbations within $\epsilon$-cubes, domains other than computer vision -- such as control systems and natural language processing -- may require more flexible input region specifications via generalised hyper-rectangles. Meanwhile, differentiable logics offer a way to encode arbitrary logical constraints as additional loss terms that guide the learning process towards satisfying these constraints. In this paper, we investigate how these two complementary approaches can be unified within a single framework for property-driven machine learning. We show that well-known properties from the literature are subcases of this general approach, and we demonstrate its practical effectiveness on a case study involving a neural network controller for a drone system. Our framework is publicly available at https://github.com/tflinkow/property-driven-ml.