Formative Artificial Intelligence: New Opportunities for Information Support of Regional Management
DOI:
https://doi.org/10.52575/2687-0932-2023-50-2-423-438Keywords:
formative artificial intelligence, generative adversarial network, ontology, regional managementAbstract
Intelligent information systems are increasingly used in the field of regional management. One of the modern basic concepts of their organization is the formative artificial intelligence. This article presents an analysis of this concept and its correlation with existing technologies of intelligent information systems, as well as an overview of the existing experience in using such formative artificial intelligence technologies as generative-adversarial neural networks and ontologies in various applied tasks to regional management. It is concluded that the necessary environment for the implementation of formative AI is an information system with agent properties. The conclusion is made about the wide possibilities of using formative intelligence in the field of information support for regional management, on the one hand, and the incomplete use of the full potential of modern intelligent information technologies, on the other.
Acknowledgements
The study was carried out within the framework of the state assignment of the IIMM KSC RAS of the Ministry of Science and Higher Education of the Russian Federation, registration number of the project: 122022800551-0.
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Список источников
AGROVOC Homepage. URL: https://www.fao.org/agrovoc/ (дата обращения: 14 апреля 2023).
EuroVoc thesaurus. URL: http://publications.europa.eu/resource/cellar/7eecbd11-c00d-11e5-9e54-01aa75ed71a1.0002.01/DOC_1 (дата обращения: 14 апреля 2023).
Список литературы
Антонов В.В., Бармина О.В., Никулина, Н.О. 2020. Поддержка принятия решений при управлении программными проектами на основе нечёткой онтологии. Онтология проектирования, 10(1 (35)), Article 1 (35).
Белов М.В. Новиков Д.А. 2021. Структура креативной деятельности. Проблемы управления, 5, Article 5.
Жихарев А.П. 2007. Состояние и перспективы использования общероссийских классификаторов в региональных АИС. Стандарты и качество, 6, 51.
Месарович М., Мако Д., Такахара И. 1973. Теория иерархических многоуровневых систем: Пер. с англ [Текст]. Мир; Книги (изданные с 1831 г. по настоящее время). https://search.rsl.ru/ru/record/01007362516
Павлов С.В., Ефремова О.А. 2017. Онтологическая модель интеграции разнородных по структуре и тематике пространственных баз данных в единую региональную базу данных. Ontology of Designing, 7(3), 323–333. https://doi.org/10.18287/2223-9537-2017-7-3-323-333
Сохова З.Б. Редько В.Г. 2021. Модель самоорганизации автономных агентов в децентрализованной среде. Проблемы управления, 2, Article 2.
Финн В.К. 2021. Искусственный интеллект: Методология, применения, философия. Изд. 2, испр. И доп. URSS.
Шевандрин А.В., Бондаренко П.В. 2020. Разработка онтологической модели государственной политики стимулирования экономического роста на федеральном и региональном уровнях. Фундаментальные исследования (Fundamental research), 4, 131–136. https://doi.org/10.17513/fr.42737
Шевандрин А.В., Калинина А.Э. 2019. Онтологическое моделирование кластерных образований в экономике регионов. Московский экономический журнал, 10, Article 10.
Allan K. 2020. What is formative AI and why should you care? IDG Connect. https://www.idgconnect.com/article/3586601/what-is-formative-ai-and-why-should-you-care.html
Andreasik J. 2022. The Ontology of the Region. Barometr Regionalny. Analizy i Prognozy, 18(1), 67–82. https://doi.org/10.56583/br.723
Avram A.-M., Pais V., Tufis D. 2021. PyEuroVoc: A Tool for Multilingual Legal Document Classification with EuroVoc Descriptors (arXiv:2108.01139). arXiv. http://arxiv.org/abs/2108.01139
Caled D., Won M., Martins B., Silva M.J. 2019. A Hierarchical Label Network for Multi-label EuroVoc Classification of Legislative Contents. В.A. Doucet, A. Isaac, K. Golub, T. Aalberg, A. Jatowt (Ред.), Digital Libraries for Open Knowledge (с. 238–252). Springer International Publishing. https://doi.org/10.1007/978-3-030-30760-8_21
Chen Q., Wang W., Huang K., De S., Coenen F. 2021. Multi-modal generative adversarial networks for traffic event detection in smart cities. Expert Systems with Applications, 177, 114939. https://doi.org/10.1016/j.eswa.2021.114939
Gao Y., Liu L., Zhang C., Wang X., Ma H. 2020. SI-AGAN: Spatial Interpolation with Attentional Generative Adversarial Networks for Environment Monitoring. ECAI 2020, 1786–1793. https://doi.org/10.3233/FAIA200293
Georgeff M. 1995. BDI Agents: From Theory to Practice. International Conference on Multiagent Systems. https://www.academia.edu/30608557/BDI_Agents_From_Theory_to_Practice
Goodfellow I.J., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S., Courville A., Bengio Y. 2014. Generative Adversarial Networks (arXiv:1406.2661). arXiv. https://doi.org/10.48550/arXiv.1406.2661
Jelesnianski C.P. 1992. SLOSH: Sea, Lake, and Overland Surges from Hurricanes. U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service.
Karwowski W., Orłowski A., Rusek M. 2019. Applications of Multilingual Thesauri for the Texts Indexing in the Field of Agriculture. В.J. Pejaś, I.El Fray, T. Hyla, J. Kacprzyk (Ред.), Advances in Soft and Hard Computing (с. 185–195). Springer International Publishing. https://doi.org/10.1007/978-3-030-03314-9_17
Kasey Panetta. 2021. 5 Trends Drive the Gartner Hype Cycle for Emerging Technologies, 2020. Gartner. https://www.gartner.com/smarterwithgartner/5-trends-drive-the-gartner-hype-cycle-for-emerging-technologies-2020
Laufer C., Schwabe D. 2017. On Modeling Political Systems to Support the Trust Process. 1–16.
Liang J., Tang W. 2020. Sequence Generative Adversarial Networks for Wind Power Scenario Generation. IEEE Journal on Selected Areas in Communications, 38(1): 110–118. https://doi.org/10.1109/JSAC.2019.2952182
Lütjens B., Leshchinskiy B., Requena-Mesa C., Chishtie F., Díaz-Rodríguez N., Boulais O., Sankaranarayanan A., Piña A., Gal Y., Raïssi C., Lavin A., Newman D. 2021. Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization (arXiv:2104.04785). arXiv. https://doi.org/10.48550/arXiv.2104.04785
Margaret Rouse. 2022. What is Generative AI? - Definition from Techopedia. Techopedia.Com. http://www.techopedia.com/definition/34633/generative-ai
Mietzsch E., Martini D., Kolshus K., Turbati A., Subirats I. 2021. How Agricultural Digital Innovation Can Benefit from Semantics: The Case of the AGROVOC Multilingual Thesaurus. Engineering Proceedings, 9(1), Article 1. https://doi.org/10.3390/engproc2021009017
Naidu J., Dr.E.N.Ganesh, Shanmugasundaram. 2019. Investigation of agricultural data for data base creation like agris for farmers welfare. 6, 437–443.
Phipps J. 2021. What Is Formative AI? Exploring the Future of AI Storage. ESF. Enterprise Storage Forum. https://www.enterprisestorageforum.com/news/formative-ai/
Qasim I., Alam M., Khan S., Khan A.W., Malik K.M., Saleem M., Bukhari S.A.C. 2020. A comprehensive review of type-2 fuzzy Ontology. Artificial Intelligence Review, 53(2): 1187–1206. https://doi.org/10.1007/s10462-019-09693-9
Rui X., Cao Y., Yuan X., Kang Y., Song W. 2021. DisasterGAN: Generative Adversarial Networks for Remote Sensing Disaster Image Generation. Remote Sensing, 13(21): Article 21. https://doi.org/10.3390/rs13214284
Russell S., Russell S.J., Norvig P., Davis E. 2010. Artificial Intelligence: A Modern Approach. Prentice Hall.
Sadek S.M., Omran W.A., Hassan M.A. M., Talaat H.E.A. 2021. Data Driven Stochastic Energy Management for Isolated Microgrids Based on Generative Adversarial Networks Considering Reactive Power Capabilities of Distributed Energy Resources and Reactive Power Costs. IEEE Access, 9, 5397–5411. https://doi.org/10.1109/ACCESS.2020.3048586
Scorza F., Casas G.B.L., Murgante B. 2012. That’s ReDO: Ontologies and Regional Development Planning. B. Murgante, O. Gervasi, S. Misra, N. Nedjah, A.M.A.C. Rocha, D. Taniar, B.O. Apduhan (Ред.), Computational Science and Its Applications – ICCSA 2012 (Т. 7334, с. 640–652). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-31075-1_48
Tarassov V.B. Svyatkina M.N. 2013. Cognitive Measurements: The Future of Intelligent Systems. Программные продукты и системы. 4, 74–82.
Wang F., Zhang Z., Liu C., Yu Y., Pang S., Duić N., Shafie-khah M., Catalão J.P.S. 2019. Generative adversarial networks and convolutional neural networks based weather classification model for day ahead short-term photovoltaic power forecasting. Energy Conversion and Management, 181, 443–462. https://doi.org/10.1016/j.enconman.2018.11.074
Wu A.N., Stouffs R., Biljecki F. 2022. Generative Adversarial Networks in the built environment: A comprehensive review of the application of GANs across data types and scales. Building and Environment, 223, 109477. https://doi.org/10.1016/j.buildenv.2022.109477
Zhang R., Isola P., Efros A.A., Shechtman E., Wang O. 2018. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. 586–595. https://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_The_Unreasonable_Effectiveness_CVPR_2018_paper.html
Zhang Y., Li Y., Zhou X., Kong X., Luo J. 2019. TrafficGAN: Off-Deployment Traffic Estimation with Traffic Generative Adversarial Networks. 2019 IEEE International Conference on Data Mining (ICDM), 1474–1479. https://doi.org/10.1109/ICDM.2019.00193
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