GitHub - acbueff/dNL-NL: One approach to explaining hierarchical levels of understanding within machine learning is the symbolic method of inductive logic programming (ILP), capable of learning logical rules that entail instances. ILP constructs logic programs from sets of ground atoms and background assumptions. Positive and negative instances are included which help define target predicates. The aim is to construct logic programs that explain provided positive sets and reject negative sets. Works have expanded ILP to allow end-to-end differentiable learning, resulting in research that combines deep learning architectures with relational models derived from ILP. However, as with the original framework, variants of differentiable ILP have focused primarily on learning discrete properties with little attention given to learning on continuous domains nor capturing non-linear properties. Given the importance of non-linearity, integrating such concepts is an open challenge still faced by ILP inspired frameworks. Capturing such non-linear properties is significantly important to a range of high impact scientific fields including robotics, physics, and computational biology, thus in this work we combine continuous and non-linear predicate functions with an extension to a differentiable ILP framework, so-called differentiable neural logic (dNL) networks. This is accomplished by exploring standard non-linear functions such as the power function and sine function, as well as operation functions such as multiplication and addition. Provided a tabular dataset comprised of non-linear mathematical relations, our non-linear dNL solver extracts the non-linear functions and assesses their performance on the training set to produce explanatory logical rules.

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One approach to explaining hierarchical levels of understanding within machine learning is the symbolic method of inductive logic programming (ILP), capable of learning logical rules that entail instances. ILP constructs logic programs from sets of ground atoms and background assumptions. Positive and negative instances are included which help d…