A key objective in field of artificial intelligence is to develop cognitive
models that can exhibit human-like intellectual capabilities. One promising
approach to achieving this is through neural-symbolic systems, which combine
the strengths of deep learning and symbolic reasoning. However, current
approaches in this area have been limited in their combining way,
generalization and interpretability. To address these limitations, we propose a
general bi-level probabilistic graphical reasoning framework called GBPGR. This
framework leverages statistical relational learning to effectively integrate
deep learning models and symbolic reasoning in a mutually beneficial manner. In
GBPGR, the results of symbolic reasoning are utilized to refine and correct the
predictions made by the deep learning models. At the same time, the deep
learning models assist in enhancing the efficiency of the symbolic reasoning
process. Through extensive experiments, we demonstrate that our approach
achieves high performance and exhibits effective generalization in both
transductive and inductive tasks.

提出了一种名为 GBPGR 的广义双层概率图推理框架，通过使用统计关系学习启发式地集成了深度学习模型和符号推理，以解决当前人工智能领域中深度学习和符号推理相结合的方法在组合方式、泛化性和可解释性方面的局限性，并通过大量实验证明了该方法在转导和归纳任务中实现了高性能和有效的泛化能力。

一种新的统计关系学习下的神经符号系统

A Novel Neural-symbolic System under Statistical Relational Learning

Despite the extensive investment and impressive recent progress at reasoning
by similarity, deep learning continues to struggle with more complex forms of
reasoning such as non-monotonic and commonsense reasoning. Non-monotonicity is
a property of non-classical reasoning typically seen in commonsense reasoning,
whereby a reasoning system is allowed (differently from classical logic) to
jump to conclusions which may be retracted later, when new information becomes
available. Neural-symbolic systems such as Logic Tensor Networks (LTN) have
been shown to be effective at enabling deep neural networks to achieve
reasoning capabilities. In this paper, we show that by combining a
neural-symbolic system with methods from continual learning, LTN can obtain a
higher level of accuracy when addressing non-monotonic reasoning tasks.
Continual learning is added to LTNs by adopting a curriculum of learning from
knowledge and data with recall. We call this process Continual Reasoning, a new
methodology for the application of neural-symbolic systems to reasoning tasks.
Continual Reasoning is applied to a prototypical non-monotonic reasoning
problem as well as other reasoning examples. Experimentation is conducted to
compare and analyze the effects that different curriculum choices may have on
overall learning and reasoning results. Results indicate significant
improvement on the prototypical non-monotonic reasoning problem and a promising
outlook for the proposed approach on statistical relational learning examples.

本文介绍一种称为连续推理的新方法，该方法将神经符号系统与知识和数据的循环学习相结合，以应对非单调推理任务，并在结果上显示了显著的改进。