Beyond Traditional Neural Networks: Toward adding Reasoning and Learning
  Capabilities through Computational Logic Techniques
Andrea Rafanelli
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).