统计关系学习下的新型神经符号系统
A Novel Neural-symbolic System under Statistical Relational Learning
- Jilin University（吉林大学）
- Griffith University（格里菲斯大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出基于统计关系学习的神经符号框架NSF-SRL，结合深度学习与符号推理，提升性能、泛化与可解释性，为通用AI研究树立新标准。
AI中文摘要：
人工智能领域的一个关键目标是开发能够展现类人智能能力的认知模型。实现这一目标的一种有前景的方法是通过神经符号系统，该系统结合了深度学习和符号推理的优势。然而，当前该领域的方法在集成、泛化和可解释性方面面临局限性。为了解决这些挑战，我们提出了一种基于统计关系学习的神经符号框架，称为NSF-SRL。该框架以互利的方式有效整合了深度学习模型与符号推理。在NSF-SRL中，符号推理的结果被用于优化和纠正深度学习模型所做的预测，而深度学习模型则提高了符号推理过程的效率。通过大量实验，我们证明了我们的方法在监督学习、弱监督和零样本学习任务中实现了高性能并展现出有效的泛化能力。此外，我们引入了一种定量策略来评估模型预测的可解释性，可视化有助于这些预测的相应逻辑规则，并提供对推理过程的洞察。我们相信这种方法为神经符号系统树立了新的标准，并将推动通用人工智能领域的未来研究。
英文摘要：
A key objective in the 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 methodologies in this area face limitations in integration, generalization, and interpretability. To address these challenges, we propose a neural-symbolic framework based on statistical relational learning, referred to as NSF-SRL. This framework effectively integrates deep learning models with symbolic reasoning in a mutually beneficial manner.In NSF-SRL, the results of symbolic reasoning are utilized to refine and correct the predictions made by deep learning models, while deep learning models enhance the efficiency of the symbolic reasoning process. Through extensive experiments, we demonstrate that our approach achieves high performance and exhibits effective generalization in supervised learning, weakly supervised and zero-shot learning tasks. Furthermore, we introduce a quantitative strategy to evaluate the interpretability of the model's predictions, visualizing the corresponding logic rules that contribute to these predictions and providing insights into the reasoning process. We believe that this approach sets a new standard for neural-symbolic systems and will drive future research in the field of general artificial intelligence.
