Deep Learning (DL) models have become popular for solving complex problems, but they have limitations such as the need for high-quality training data, lack of transparency, and robustness issues. Neuro-Symbolic AI has emerged as a promising approach combining the strengths of neural networks and symbolic reasoning. Symbolic knowledge injection (SKI) techniques are a popular method to incorporate symbolic knowledge into sub-symbolic systems. This work proposes solutions to improve the knowledge injection process and integrate elements of ML and logic into multi-agent systems (MAS).

翻译：深度学习模型在解决复杂问题方面已变得流行，但其存在对高质量训练数据的依赖、缺乏透明性以及鲁棒性不足等局限。神经符号人工智能作为结合神经网络与符号推理优点的有前景方法应运而生。符号知识注入技术是将符号知识融入亚符号系统的常用方法。本研究提出了改进知识注入过程的解决方案，并将机器学习与逻辑元素集成到多智能体系统中。