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 bridges the gap between 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 方法结合神经结构、知识代表性和推理。 但是,它们一直在努力应对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),这是一个全新的端到端模型,在深层学习结构的潜层中进行关系推理,在这种深层学习结构中,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只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,计算地面原子之间的较高关系。可以明确知道考虑的关系,如逻辑公式定义的关系,或定义为地面原子群体之间不固定的关联。R2N可以应用到纯粹象征性的任务,或作为神经同步平台,将不同问题中的学习和推理与象征性和基于特征的实体结合起来。提议的模型缩小了以前在实验性方法的可变性或直观性方面受到限制的神经平衡方法之间的差距。 拟议的模型显示的是,在实验性结构中显示为不同程度的结果。