  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).
