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.

提出并实现了完全声明性的神经谓词的通用框架，从而扩展到完全声明性的 NeSy 框架。首先证明声明性扩展在保持学习和推理能力的同时，能够在仅通过单个查询类型进行训练的情况下回答任意查询。

走向完全声明性神经符号语言

Towards a fully declarative neuro-symbolic language

We introduce DeepProbLog, a neural probabilistic logic programming language
that incorporates deep learning by means of neural predicates. We show how
existing inference and learning techniques of the underlying probabilistic
logic programming language ProbLog can be adapted for the new language. We
theoretically and experimentally demonstrate that DeepProbLog supports (i) both
symbolic and subsymbolic representations and inference, (ii) program induction,
(iii) probabilistic (logic) programming, and (iv) (deep) learning from
examples. To the best of our knowledge, this work is the first to propose a
framework where general-purpose neural networks and expressive
probabilistic-logical modeling and reasoning are integrated in a way that
exploits the full expressiveness and strengths of both worlds and can be
trained end-to-end based on examples.

DeepProbLog 是一种神经概率逻辑编程语言，通过神经谓词结合深度学习。它支持符号和子符号表示和推断、程序归纳、概率编程和深度学习，并结合了通用神经网络和表达式概率逻辑建模和推理。