Neuro-symbolic methods integrate neural architectures, knowledge representation and reasoning. However, they have been struggling at both dealing with the intrinsic uncertainty of the observations and scaling to real-world applications. This paper presents Relational Reasoning Networks (R2N), a novel end-to-end model that performs relational reasoning in the latent space of a deep learner architecture, where the representations of constants, ground atoms and their manipulations are learned in an integrated fashion. Unlike flat architectures like Knowledge Graph Embedders, which can only represent relations between entities, R2Ns define an additional computational structure, accounting for higher-level relations among the ground atoms. The considered relations can be explicitly known, like the ones defined by logic formulas, or defined as unconstrained correlations among groups of ground atoms. R2Ns can be applied to purely symbolic tasks or as a neuro-symbolic platform to integrate learning and reasoning in heterogeneous problems with both symbolic and feature-based represented entities. The proposed model overtakes the limitations of previous neuro-symbolic methods that have been either limited in terms of scalability or expressivity. The proposed methodology is shown to achieve state-of-the-art results in different experimental settings.
翻译:Neuro-symbolic 方法将神经结构、知识代表性和推理结合起来。 但是,它们一直在努力处理观测的内在不确定性和向现实世界应用的扩展。本文件展示了关系理性网络(R2N),这是一个新型端到端模型,在深层学习结构的潜伏空间中进行关系推理,在深层学习结构中,常数、地面原子及其操控的表述以综合的方式学习。与知识图形嵌入器等仅能代表实体之间关系的平板结构不同,R2Ns定义了额外的计算结构,计算了地面原子之间的更高层次关系。可以明确知道所考虑的关系,就像逻辑公式定义的网络一样,或者被定义为地面原子群体之间不受约束的关联。R2N可以应用到纯粹象征性的任务,或者作为神经同步平台,将学习和推理与具有象征意义和特征的实体的复杂问题结合起来。提议的模型超越了先前的神经平衡方法的局限性,这些方法要么是有限的,要么是在不同的实验性方法中显示的缩度。