Differentiable Logic Machines
  The integration of reasoning, learning, and decision-making is key to build
more general AI systems. As a step in this direction, we propose a novel
neural-logic architecture that can solve both inductive logic programming (ILP)
and deep reinforcement learning (RL) problems. Our architecture defines a
restricted but expressive continuous space of first-order logic programs by
assigning weights to predicates instead of rules. Therefore, it is fully
differentiable and can be efficiently trained with gradient descent. Besides,
in the deep RL setting with actor-critic algorithms, we propose a novel
efficient critic architecture. Compared to state-of-the-art methods on both ILP
and RL problems, our proposition achieves excellent performance, while being
able to provide a fully interpretable solution and scaling much better,
especially during the testing phase.
