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

With the technology of the time, Kowalski's seminal 1974 paper {\em Predicate
Logic as a Programming Language} was a breakthrough for the use of logic in
computer science. It introduced two fundamental ideas: on the declarative side,
the use of the Horn clause logic fragment of classical logic, which was soon
extended with negation as failure, on the procedural side the procedural
interpretation which made it possible to write algorithms in the formalism.
Since then, strong progress was made both on the declarative understanding of
the logic programming formalism and in automated reasoning technologies,
particularly in SAT solving, Constraint Programming and Answer Set Programming.
This has paved the way for the development of an extension of logic programming
that embodies a more pure view of logic as a modelling language and its role
for problem solving.
In this paper, we present the \idp language and system. The language is
essentially classical logic extended with one of logic programmings most
important contributions to knowledge representation: the representation of
complex definitions as rule sets under well-founded semantics. The system is a
knowledge base system: a system in which complex declarative information is
stored in a knowledge base which can be used to solve different computational
problems by applying multiple forms of inference. In this view, theories are
declarative modellings, bags of information, descriptions of possible states of
affairs. They are neither procedures nor descriptions of computational
problems. As such, the \idp language and system preserve the fundamental idea
of a declarative reading of logic programs, while they break with the
fundamental idea of the procedural interpretation of logic programs.

介绍了基于纯逻辑物理模型语言的扩展的逻辑编程形式，其中的复杂定义是作为规则集的表示，并且通过多种形式的推理来解决不同的计算问题。