Formal Methods and Intelligent Systems: Trends and Advances in Theoretical and Applied Informatics
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 20 December 2025 | Viewed by 20
Special Issue Editors
Interests: computer vision; Machine Learning; ophthalmic imaging; reinforcement learning; image processing; clinical validation; color retinal fundus images
Interests: formal methods; model checking; real-time systems; theoretical computer science; automata theory; verification; SAT
Special Issue Information
Dear Colleagues,
Formal methods and intelligent systems represent two distinct, yet increasingly complementary, areas of computer science. Formal methods focus on mathematical found techniques for the specification, development, and verification of complex systems, proving their correctness and reliability. In contrast, intelligent systems—often encompassing heuristics, such as machine learning, deep learning, approximate methods—prioritize adaptability, autonomy, and data-driven decision support, sometimes at the expense of formal guarantees.
Despite these differences, recent advances suggest a promising convergence. The growing complexity and critical nature of intelligent methods demand more rigorous foundations, while formal methods benefit from the adaptability and learning capabilities of intelligent components. This Special Issue aims to explore the advances in the areas of formal methods and intelligent systems, to investigate this emerging synergy, and to foster dialog between these two domains.
For this Special Issue, we invite original research that investigates the areas of formal methods, intelligent systems and the interplay between them. A particular emphasis is placed on intelligent methods, including their foundations, theoretical developments, and applications to both simple and complex data types. Topics include, but are not limited to, the following:
- Machine learning and its applications;
- Machine learning and theoretical foundations;
- Formal verification of neural networks;
- Formal verification of reinforcement learning policies;
- Safe deployment of ML systems;
- Formal specification and modeling;
- Formal verification and model checking.
Dr. Tomasz Krzywicki
Prof. Dr. Andrzej Zbrzezny
Dr. Toktam Ghafarian Mabhoot
Guest Editors
Manuscript Submission Information
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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
- model verification
- machine learning
- deep learning
- reinforcement learning
- model checking of ML
- trustworthy AI
- cyber-physical systems
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