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. We analyse how learning the inference algorithm gives
rise to representations of atoms, literals and rules within logic programs and
evaluate against increasing lengths of predicate and constant symbols as well
as increasing steps of multi-hop reasoning.

探索如何使用基于 RNN 的迭代神经网络将符号逻辑定义为字符级逻辑程序，使其在高维向量空间中学习推理，进一步分析学习推理算法如何产生逻辑程序中的原子、文字和规则表示，并针对谓词和常量符号的长度以及多次跳跃推理的步数进行评估。