Neuro-Symbolic AI for Compliance Checking of Electrical Control Panels
Vito Barbara Massimo Guarascio Nicola Leone Giuseppe Manco Alessandro Quarta Francesco Ricca Ettore Ritacco
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.