Математическая сингулярность: как модели поведения роя превращаются в роевой интеллект и позволяют строить образовательную траекторию обучения

Статья исследует феномен математической сингулярности в контексте роевого интеллекта и его применение для построения адаптивных образовательных траекторий. На примере модели движения муравьиной колонии (Ant Colony Optimization) демонстрируется, как локальные взаимодействия агентов приводят к глобально оптимальным решениям, преодолевая ограничения индивидуального поведения. Анализируется аналогия между поиском кратчайшего пути в природе и выбором персонализированной траектории обучения. Рассматриваются технологические и этические аспекты внедрения роевых алгоритмов в образовательные системы, а также перспективы создания самоорганизующихся платформ, способных адаптироваться к потребностям учащихся. Статья подчеркивает, что сингулярность в роевых системах открывает путь к созданию "умных" образовательных сред, где траектории формируются не сверху вниз, а через взаимодействие учащихся, данных и адаптивных алгоритмов.

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13. Excerpts from "!!! from-animal-collective-behaviors-to-swarm-robotic-84dsxa35.pdf" (The full bibliographic details are provided within the document itself as: Natl Sci Rev, 2023, Vol. 10, nwad040, Downloaded from https://academic.oup.com/nsr/article/10/5/nwad040/7043485 by guest on 01 October 2023).

14. Excerpts from "01.pdf" (The full bibliographic details are: International Journal of Research in Business and Social Science (2147-4478), © 2023 by the authors. Licensee SSBFNET, Istanbul, Turkey. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).).

15. Excerpts from "06.pdf' (The full bibliographic details are: Entropy 2023, 25, 710. https://doi.org/10.3390/e25050710).

16. Excerpts from "08.pdf' (This excerpt references several works by Bruce J. MacLennan and others, making it difficult to cite as a single entity. Some referenced works include: MacLennan, B.J. (2014). Coordinating massive robot swarms. International Journal of Robotics Applications and Technologies, 2(2), 1-19; MacLennan, B.J. (2015). The morphogenetic path to programmable matter. Proceedings of the IEEE, 103(7), 1226-1232; MacLennan, B.J. (2018). Coordinating swarms of microscopic agents to assemble complex structures. In Swarm Intelligence, Vol. 1: Principles, Current Algorithms and Methods (pp. 583-612). Institution of Engineering and Technology.).

17. Excerpts from "09.pdf" (This appears to be an internal report supported by NASA, the specific citation details are not fully present in the excerpt).

18. Excerpts from "15.pdf" (This excerpt contains references to various works in swarm intelligence and related fields, making it challenging to cite as a single source. Examples include: Kennedy J., Eberhardt R. (1995) Particle swarm optimization, Proc. IEEE Int. Conf. Neural Network, 1942-1948; Weber H. (1974) GrundriMer Insektenkunde, Gutav Fischer Verlag, Stuttgart.)