# Neuro-Symbolic AI for Compliance Checking of Electrical Control Panels

arXiv: https://arxiv.org/abs/2305.10113 · HTML: https://mlsanity.com/paper/2305.10113/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning](https://mlsanity.com/paper/1909.02151/) (2019)
- [End-to-End Differentiable Proving](https://mlsanity.com/paper/1705.11040/) (2017)
- [Logic Tensor Networks for Semantic Image Interpretation](https://mlsanity.com/paper/1705.08968/) (2017)
- [The Seventh Answer Set Programming Competition: Design and Results](https://mlsanity.com/paper/1904.09134/) (2019)
- [Combining Answer Set Programming and Domain Heuristics for Solving Hard Industrial Problems (Application Paper)](https://mlsanity.com/paper/1608.00730/) (2016)

## Built on by (most cited first, 6 of 6)

- [Visual Graph Question Answering with ASP and LLMs for Language Parsing](https://mlsanity.com/paper/2502.09211/) (2025)
- [RepV: Safety-Separable Latent Spaces for Scalable Neurosymbolic Plan Verification](https://mlsanity.com/paper/2510.26935/) (2025)
- [Neural Decision-Propagation for Answer Set Programming](https://mlsanity.com/paper/2605.01797/) (2026)
- [Improving ASP-based ORS Schedules through Machine Learning Predictions](https://mlsanity.com/paper/2507.16454/) (2025)
- [Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation](https://mlsanity.com/paper/2507.20014/) (2025)
- [The Dual Role of Abstracting over the Irrelevant in Symbolic Explanations: Cognitive Effort vs. Understanding](https://mlsanity.com/paper/2602.03467/) (2026)

## Abstract (arXiv)

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.