A modern approach to calculating the abilities and capabilities of military units to perform tasks using artificial intelligence
The research examines the issue of assessing the capabilities of military units, which is highly relevant in modern conditions. Although formula-based evaluation methods are still used today, they require improvement, particularly in refining results and increasing calculation speed. The advantages...
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| Datum: | 2025 |
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Інститут проблем реєстрації інформації НАН України
2025
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| Online Zugang: | http://drsp.ipri.kiev.ua/article/view/345668 |
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| Назва журналу: | Data Recording, Storage & Processing |
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Data Recording, Storage & Processing| _version_ | 1856543238553337856 |
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| author | Бойченко, А. В. Додонов, В. О. Залужний, В. Ф. Ізварін, Є. І. |
| author_facet | Бойченко, А. В. Додонов, В. О. Залужний, В. Ф. Ізварін, Є. І. |
| author_sort | Бойченко, А. В. |
| baseUrl_str | |
| collection | OJS |
| datestamp_date | 2025-12-21T03:44:45Z |
| description | The research examines the issue of assessing the capabilities of military units, which is highly relevant in modern conditions. Although formula-based evaluation methods are still used today, they require improvement, particularly in refining results and increasing calculation speed. The advantages of this approach include ease of use, the absence of a need for large volumes of input data, minimal computational resource requirements, as well as the transparency and clarity of the algorithms. However, this approach also has significant limitations, as it covers a narrow set of factors and cannot adapt to changes in the combat environment. A problem arises when the calculated value falls on the borderline between capabi-lity categories. In such cases, the risk of making erroneous managerial decisions regarding a unit's ability to execute a combat mission increases.
A system using neural networks for capability assessment is proposed, which would significantly enhance the accuracy, objectivity, and efficiency of calculating the capabilities and readiness of military units. Since the principle of capability assessment has a hierarchical structure, an effective solution would be to create a tree-like or network-centric architecture utilizing multiple interconnected neural networks, each responsible for calculating a specific capability it was trained for. Such a system could compute multiple capabilities simultaneously, greatly improving the speed at which command receives objective information — something the formula-based approach cannot achieve. The results of these calculations could be integrated into military analytics systems, enhancing situational awareness for command and helping to avoid knowingly unfeasible missions. Tabl.: 1. Fig.: 5. Refs: 14 titles. |
| first_indexed | 2026-02-08T08:00:03Z |
| format | Article |
| id | drspiprikievua-article-345668 |
| institution | Data Recording, Storage & Processing |
| language | Ukrainian |
| last_indexed | 2026-02-08T08:00:03Z |
| publishDate | 2025 |
| publisher | Інститут проблем реєстрації інформації НАН України |
| record_format | ojs |
| spelling | drspiprikievua-article-3456682025-12-21T03:44:45Z A modern approach to calculating the abilities and capabilities of military units to perform tasks using artificial intelligence Сучасний підхід до розрахунків можливостей і спроможностей виконання завдань військовими підрозділами з використанням штучного інтелекту Бойченко, А. В. Додонов, В. О. Залужний, В. Ф. Ізварін, Є. І. нейромережа, спроможність, можливість, штучний інтелект neural network, capability, readiness, artificial intelligence The research examines the issue of assessing the capabilities of military units, which is highly relevant in modern conditions. Although formula-based evaluation methods are still used today, they require improvement, particularly in refining results and increasing calculation speed. The advantages of this approach include ease of use, the absence of a need for large volumes of input data, minimal computational resource requirements, as well as the transparency and clarity of the algorithms. However, this approach also has significant limitations, as it covers a narrow set of factors and cannot adapt to changes in the combat environment. A problem arises when the calculated value falls on the borderline between capabi-lity categories. In such cases, the risk of making erroneous managerial decisions regarding a unit's ability to execute a combat mission increases. A system using neural networks for capability assessment is proposed, which would significantly enhance the accuracy, objectivity, and efficiency of calculating the capabilities and readiness of military units. Since the principle of capability assessment has a hierarchical structure, an effective solution would be to create a tree-like or network-centric architecture utilizing multiple interconnected neural networks, each responsible for calculating a specific capability it was trained for. Such a system could compute multiple capabilities simultaneously, greatly improving the speed at which command receives objective information — something the formula-based approach cannot achieve. The results of these calculations could be integrated into military analytics systems, enhancing situational awareness for command and helping to avoid knowingly unfeasible missions. Tabl.: 1. Fig.: 5. Refs: 14 titles. Розглянуто питання оцінки спроможностей військових підрозділів, що є надзвичайно актуальним у сучасних умовах. Показано що формульні методики оцінювання потребують удосконалення, зокрема уточнення результатів та підвищення швидкості розрахунків особливо у випадках, коли розраховане значення знаходиться на межі між категоріями значень спроможностей. Запропоновано систему із використанням нейронних мереж для оцінки спроможностей, яка дозволить значно підвищити точність, об’єктивність і оперативність розрахунку спромож-ностей і можливостей військових підрозділів. Результати можуть бути інтегрованими у системи військової аналітики, що покращить ситуаційну обізнаність командування та дозволить уникати свідомо нездійсненних завдань. Інститут проблем реєстрації інформації НАН України 2025-09-16 Article Article application/pdf http://drsp.ipri.kiev.ua/article/view/345668 10.35681/1560-9189.2025.27.2.345668 Data Recording, Storage & Processing; Vol. 27 No. 2 (2025); 111-121 Регистрация, хранение и обработка данных; Том 27 № 2 (2025); 111-121 Реєстрація, зберігання і обробка даних; Том 27 № 2 (2025); 111-121 1560-9189 uk http://drsp.ipri.kiev.ua/article/view/345668/334399 Авторське право (c) 2025 Реєстрація, зберігання і обробка даних |
| spellingShingle | neural network capability readiness artificial intelligence Бойченко, А. В. Додонов, В. О. Залужний, В. Ф. Ізварін, Є. І. A modern approach to calculating the abilities and capabilities of military units to perform tasks using artificial intelligence |
| title | A modern approach to calculating the abilities and capabilities of military units to perform tasks using artificial intelligence |
| title_alt | Сучасний підхід до розрахунків можливостей і спроможностей виконання завдань військовими підрозділами з використанням штучного інтелекту |
| title_full | A modern approach to calculating the abilities and capabilities of military units to perform tasks using artificial intelligence |
| title_fullStr | A modern approach to calculating the abilities and capabilities of military units to perform tasks using artificial intelligence |
| title_full_unstemmed | A modern approach to calculating the abilities and capabilities of military units to perform tasks using artificial intelligence |
| title_short | A modern approach to calculating the abilities and capabilities of military units to perform tasks using artificial intelligence |
| title_sort | modern approach to calculating the abilities and capabilities of military units to perform tasks using artificial intelligence |
| topic | neural network capability readiness artificial intelligence |
| topic_facet | нейромережа спроможність можливість штучний інтелект neural network capability readiness artificial intelligence |
| url | http://drsp.ipri.kiev.ua/article/view/345668 |
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