Neurosymbolic Artificial Intelligence (NeSy) has emerged as a promising direction to integrate low level perception with high level reasoning. Unfortunately, little attention has been given to developing NeSy systems tailored to temporal/sequential problems. This entails reasoning symbolically over sequences of subsymbolic observations towards a target prediction. We show that using a probabilistic semantics symbolic automata, which combine the power of automata for temporal structure specification with that of propositional logic, can be used to reason efficiently and differentiably over subsymbolic sequences. The proposed system, which we call NeSyA (Neuro Symbolic Automata), is shown to either scale or perform better than existing NeSy approaches when applied to problems with a temporal component.

翻译：神经符号人工智能（NeSy）已成为融合低层感知与高层推理的一个有前景的方向。遗憾的是，目前鲜有研究关注开发针对时序/序列问题的专用NeSy系统。这需要基于子符号观测序列进行符号化推理，以实现目标预测。我们证明，采用概率语义的符号自动机——其结合了自动机在时序结构规约方面的能力与命题逻辑的表达力——能够高效且可微分地对子符号序列进行推理。所提出的系统（我们称之为NeSyA，即神经符号自动机）在应用于具有时序成分的问题时，相较于现有NeSy方法，展现出更优的扩展性或性能表现。