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

Andrea Rafanelli
(University of Pisa, Italy; University of L'Aquila, Italy)

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).

In Enrico Pontelli, Stefania Costantini, Carmine Dodaro, Sarah Gaggl, Roberta Calegari, Artur D'Avila Garcez, Francesco Fabiano, Alessandra Mileo, Alessandra Russo and Francesca Toni: Proceedings 39th International Conference on Logic Programming (ICLP 2023), Imperial College London, UK, 9th July 2023 - 15th July 2023, Electronic Proceedings in Theoretical Computer Science 385, pp. 416–422.
Published: 12th September 2023.

ArXived at: https://dx.doi.org/10.4204/EPTCS.385.51 bibtex PDF
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