Neuro-Symbolic AI for Compliance Checking of Electrical Control Panels

• URL: http://arxiv.org/abs/2305.10113v1
• Date: Wed, 17 May 2023 10:29:02 GMT
• Title: Neuro-Symbolic AI for Compliance Checking of Electrical Control Panels
• Authors: Vito Barbara (1), Massimo Guarascio (2), Nicola Leone (1), Giuseppe Manco (2), Alessandro Quarta (3), Francesco Ricca (1), Ettore Ritacco (4) ((1) University of Calabria, (2) ICAR-CNR, (3) Sapienza University of Rome, (4) University of Udine)
• Abstract summary: We propose a Neuro-Symbolic approach for automating the compliance verification of the electrical control panels. Our approach is based on the combination of Deep Learning techniques with Answer Set Programming (ASP) and allows for identifying possible anomalies and errors in the final product. The experiments conducted on a real test case provided by an Italian Company demonstrate the effectiveness of the proposed approach.
• Score: 47.187609203210705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial Intelligence plays a main role in supporting and improving smart manufacturing and Industry 4.0, by enabling the automation of different types of tasks manually performed by domain experts. In particular, assessing the compliance of a product with the relative schematic is a time-consuming and prone-to-error process. In this paper, we address this problem in a specific industrial scenario. In particular, we define a Neuro-Symbolic approach for automating the compliance verification of the electrical control panels. Our approach is based on the combination of Deep Learning techniques with Answer Set Programming (ASP), and allows for identifying possible anomalies and errors in the final product even when a very limited amount of training data is available. The experiments conducted on a real test case provided by an Italian Company operating in electrical control panel production demonstrate the effectiveness of the proposed approach.

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