Neuro-symbolic systems (NeSy), which claim to combine the best of both learning and reasoning capabilities of artificial intelligence, are missing a core property of reasoning systems: Declarativeness. The lack of declarativeness is caused by the functional nature of neural predicates inherited from neural networks. We propose and implement a general framework for fully declarative neural predicates, which hence extends to fully declarative NeSy frameworks. We first show that the declarative extension preserves the learning and reasoning capabilities while being able to answer arbitrary queries while only being trained on a single query type.

翻译：神经符号系统（NeSy）旨在结合人工智能的学习与推理能力，却缺失了推理系统的核心特性：声明性。这种声明性的缺乏源于从神经网络继承而来的神经谓词的功能性本质。我们提出并实现了一个完全声明式神经谓词的通用框架，从而扩展至完全声明式的NeSy框架。我们首先证明，该声明式扩展在保持学习与推理能力的同时，能够仅通过单一查询类型的训练即可回答任意查询。