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Symmetry
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Symmetry/Asymmetry and Artificial Intelligence: Models, Methods, and Applications
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Symmetry/Asymmetry and Artificial Intelligence: Models, Methods, and Applications

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1625

Special Issue Editors

Prof. Dr. Soumia Ziti

E-Mail Website
Guest Editor
Intelligent Processing and Security of Systems, Faculty of Science, Mohammed V University in Rabat, Rabat 1014, RP, Morocco
Interests: computer science; software engineering; graph theory; data science; artificial intelligence
Prof. Dr. Nassim Kharmoum

E-Mail Website
Guest Editor
Intelligent Processing and Security of Systems, Faculty of Science, Mohammed V University in Rabat, Rabat 1014, RP, Morocco
Interests: software engineering; artificial intelligence; analysis and conceptual modeling; software requirements; multi-agent systems; generative AI; digital health
Dr. Al Idrissi Najib

E-Mail Website
Guest Editor
Precision Medicine and One Health Laboratory; Faculty of Medicine, Mohammed VI University of Sciences and Health, Casablanca 82403, Morocco
Interests: digital health; personalized medicine; cancer; microbiome; tissue engineering and biomaterials; cell therapy and regenerative medicine

Special Issue Information

Dear Colleagues,

This Special Issue, “Symmetry/Asymmetry and Artificial Intelligence: Models, Methods, and Applications”, invites high-quality contributions exploring the interplay between symmetry principles and artificial intelligence. Symmetry and asymmetry are fundamental concepts that underpin many aspects of data representation, model design, and computational efficiency. In artificial intelligence, they emerge naturally in machine learning, deep learning, graph theory, and intelligent systems, where exploiting symmetry can reduce complexity, enhance generalization, and improve interpretability, while asymmetry often drives adaptability, robustness, and novel learning strategies.

The aim of this Special Issue is to bring together theoretical developments, methodological innovations, and real-world applications that reveal how symmetry and asymmetry shape intelligent models and algorithms. We encourage submissions addressing new frameworks, optimization strategies, and domain-specific applications where symmetry or asymmetry plays a critical role. Both fundamental research and applied studies across diverse areas of AI are welcome.

Prof. Dr. Soumia Ziti
Prof. Dr. Nassim Kharmoum
Dr. Al Idrissi Najib
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • symmetry and asymmetry in AI
  • machine learning and deep learning
  • graph theory and graph neural networks
  • intelligent systems and optimization
  • data representation and structural patterns
  • algorithmic efficiency and robustness
  • application symmetry in computation
  • adaptive and asymmetric models

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

27 pages, 1237 KB  
Article
Constraint, Asymmetry, and Meaning: A Cybernetic Reinterpretation of Probabilistic Emergence Across Complex Systems
by Ezra N. S. Lockhart
Symmetry 2026, 18(3), 518; https://doi.org/10.3390/sym18030518 - 18 Mar 2026
Viewed by 583
Abstract
This study develops a Constraint-Driven Model of Intelligence to explain the emergence of structured meaning in complex systems, reconciling probability and cybernetics. It applies a conceptual–analytic procedure, conducted entirely through logical reasoning and theoretical analysis, without empirical measurement, data acquisition, experimental manipulation, or [...] Read more.
This study develops a Constraint-Driven Model of Intelligence to explain the emergence of structured meaning in complex systems, reconciling probability and cybernetics. It applies a conceptual–analytic procedure, conducted entirely through logical reasoning and theoretical analysis, without empirical measurement, data acquisition, experimental manipulation, or statistical testing, and is therefore methodologically separate from empirical artificial intelligence research. Phenomena such as model collapse are cited as theoretical instances for epistemic argumentation, without asserting empirical verification. Building on Émile Borel’s Infinite Monkey Theorem, which demonstrates the theoretical inevitability of order in unbounded stochastic processes, and Gregory Bateson’s principle of negative explanation, which defines structure as the result of systematically eliminated alternatives, the analysis formalizes how constraints break ergodicity and generate asymmetry. Shannon’s entropy quantifies the informational effects of constraints, while Simon’s bounded rationality and Turing’s algorithmic limits show how cognitive and computational boundaries produce tractable outcomes. Applied to modern AI, the model accounts for model collapse in recursive training, showing that the loss of asymmetric constraints produces low-entropy, repetitive outputs, demonstrating the epistemic necessity of constraint regulation. Comparing probabilistic and cybernetic accounts of emergence, the study shows that structured intelligence arises not from stochastic exploration alone, but from bounded, recursive, selective processes. This model is transdisciplinary, formalizing how constraints from socioeconomic pressures to subcultural circulation shape diversity, innovation, and functional asymmetry, establishing a generalizable cybernetic epistemology for the generation of structured intelligence and meaning across domains. By formalizing these concepts through set-theoretic derivations and integrative synthesis, this non-empirical model advances a cybernetic epistemology, separate from quantitative AI evaluations or experimental designs. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry and Artificial Intelligence: Models, Methods, and Applications)
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18 pages, 4729 KB  
Article
Improved YOLOv5s-Based Crack Detection Method for Sealant-Spraying Devices
by Weiyi Kong, Hua Ding, Qingzhang Cheng, Ning Li, Xiaochun Sun and Xiaoxin Dong
Symmetry 2025, 17(12), 2089; https://doi.org/10.3390/sym17122089 - 5 Dec 2025
Cited by 1 | Viewed by 562
Abstract
The manual spraying of sealant on train side doors is associated with high costs and significant safety risks. To address this challenge, this study proposes an automated crack localization method for sealant-spraying devices by enhancing the YOLOv5s network, with a specific focus on [...] Read more.
The manual spraying of sealant on train side doors is associated with high costs and significant safety risks. To address this challenge, this study proposes an automated crack localization method for sealant-spraying devices by enhancing the YOLOv5s network, with a specific focus on leveraging principles of symmetry. First, an automated sealant-spraying device is designed for operation and data acquisition. Geometric symmetry is then exploited through Zhang’s camera calibration method to accurately establish the two-dimensional mapping between spatial coordinates and the image plane, a process fundamental to spatial reasoning. The core of our approach lies in introducing structural and computational symmetry into the deep learning model. The original YOLOv5s network is improved by integrating the Selective Context Convolutional module and the Skew Intersection over Union (IoU) Loss function, which streamline computation and boost detection accuracy. Furthermore, we replace the standard C3 module with an improved version that incorporates a Reparameterization Visual Transfer block, enhancing feature representation through structural re-parameterization symmetry between training and inference phases. Validation using data from a coal handling facility demonstrates that the improved YOLOv5s model achieves superior performance in precision, mAP@0.5, and recall compared to the original. The results underscore the critical role of geometric and architectural symmetry in developing robust and efficient vision systems for industrial automation. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry and Artificial Intelligence: Models, Methods, and Applications)
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