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

Autor:	Rafanelli, Andrea
Rok vydání:	2023
Předmět:	Computer Science - Artificial Intelligence Computer Science - Machine Learning Computer Science - Logic in Computer Science Computer Science - Multiagent Systems
Zdroj:	EPTCS 385, 2023, pp. 416-422
Druh dokumentu:	Working Paper
DOI:	10.4204/EPTCS.385.51
Popis:	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). Comment: In Proceedings ICLP 2023, arXiv:2308.14898
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2308.15899 Zobrazit plný text záznamu View this record from Arxiv