Beyond Traditional Neural Networks: Toward adding Reasoning and Learning Capabilities through Computational Logic Techniques

• URL: http://arxiv.org/abs/2308.15899v1
• Date: Wed, 30 Aug 2023 09:09:42 GMT
• Title: Beyond Traditional Neural Networks: Toward adding Reasoning and Learning Capabilities through Computational Logic Techniques
• Authors: Andrea Rafanelli (University of Pisa, Italy, University of L'Aquila, Italy)
• Abstract summary: This work proposes solutions to improve the knowledge injection process and integrate elements of ML and logic into multi-agent systems. Neuro-Symbolic AI has emerged as a promising approach combining the strengths of neural networks and symbolic reasoning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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).

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