Inference in relational neural machines

Integrating logic reasoning and deep learning from sensory data is a key challenge to develop artificial agents able to operate in complex environments. Whereas deep learning can operate at a large scale thanks to recent hardware advancements (GPUs) as well as other important technical advancements like Stochastic Gradient Descent, logic inference can not be executed over large reasoning tasks, as it requires to consider a combinatorial number of possible assignments. Relational Neural Machines (RNMs) have been recently introduced in order to co-train a deep learning machine and a first-order probabilistic logic reasoner in a fully integrated way. In this context, it is crucial to avoid the logic inference to become a bottleneck, preventing the application of the methodology to large scale learning tasks. This paper proposes and compares different inference schemata for Relational Neural Machines together with some preliminary results to show the effectiveness of the proposed methodologies.