  We propose a novel learning paradigm for Deep Neural Networks (DNN) by using
Boolean logic algebra. We first present the basic differentiable operators of a
Boolean system such as conjunction, disjunction and exclusive-OR and show how
these elementary operators can be combined in a simple and meaningful way to
form Neural Logic Networks (NLNs). We examine the effectiveness of the proposed
NLN framework in learning Boolean functions and discrete-algorithmic tasks. We
demonstrate that, in contrast to the implicit learning in MLP approach, the
proposed neural logic networks can learn the logical functions explicitly that
can be verified and interpreted by human. In particular, we propose a new
framework for learning the inductive logic programming (ILP) problems by
exploiting the explicit representational power of NLN. We show the proposed
neural ILP solver is capable of feats such as predicate invention and recursion
and can outperform the current state of the art neural ILP solvers using a
variety of benchmark tasks such as decimal addition and multiplication, and
sorting on ordered list.

Machine Learning;Artificial Intelligence