  We propose a novel paradigm for solving Inductive Logic Programming (ILP)
problems via deep recurrent neural networks. This proposed ILP solver is
designed based on differentiable implementation of the deduction via forward
chaining. In contrast to the majority of past methods, instead of searching
through the space of possible first-order logic rules by using some restrictive
rule templates, we directly learn the symbolic logical predicate rules by
introducing a novel differentiable Neural Logic (dNL) network. The proposed dNL
network is able to learn and represent Boolean functions efficiently and in an
explicit manner. We show that the proposed dNL-ILP solver supports desirable
features such as recursion and predicate invention. Further, we investigate the
performance of the proposed ILP solver in classification tasks involving
benchmark relational datasets. In particular, we show that our proposed method
outperforms the state of the art ILP solvers in classification tasks for
Mutagenesis, Cora and IMDB datasets.
