DeepLogic: Towards End-to-End Differentiable Logical Reasoning

Understand

Combining machine learning with logic-based expert systems in order to get the best of both worlds are becoming increasingly popular.

• However, to what extent machine learning can already learn to reason over rule-based knowledge is still an open problem.
• In this paper, we explore how symbolic logic, defined as logic programs at a character level, is learned to be represented in a high-dimensional vector space using RNN-based iterative neural networks to perform reasoning.
• We create a new dataset that defines 12 classes of logic programs exemplifying increased level of complexity of logical reasoning and train the networks in an end-to-end fashion to learn whether a logic program entails a given query.