Аннотації

Аналіз задач інтелектуальної системи проведення конкурсів проєктів

Автор(и):
Гнатієнко Г., Доманецька І., Гнатієнко О., Хроленко Я.
Автор(и) (англ)
Hnatiienko H., Domanetska I., Hnatiienko O., Khrolenko Y.
Дата публікації:

29.11.2024

Анотація (укр):

Практичний досвід проведення конкурсів проєктів засвідчує, що це складна багатовимірна задача. Конкурси, як правило, характеризуються багатьма особливостями, які суттєво впливають на правила і технологію їх проведення, критерії та форми оцінювання. Об'єктивне оцінювання проєктів вимагає залучення широкого кола експертів, що не завжди можливо. Використання штучного інтелекту людиною є ключовим фактором досягнення оптимальних результатів у багатьох галузях, від бізнесу до науки і техніки. Стаття присвячена проблемам побудови інтелектуальної системи прийняття рішень для конкурсів проєктів для широкого класу практичних проблемних областей. У статті наведено огляд наукових праць у цій галузі. Аналіз наукових публікацій засвідчив, що є декілька режимів застосування технологій штучного інтелекту в діяльності людини, які визначають ступінь його використання. На основі практичного досвіду розроблення та впровадження інтелектуальних систем, отриманого авторами цієї статті, узагальнено основні проблеми, що виникають у процесі обробки інтелектуальних даних, визначено низку завдань та запропоновано схеми їх вирішення. Детально розглянуто етапи проведення змагань, які потребують підтримки за допомогою інтелектуальної системи підтримки прийняття рішень. Наведено перелік завдань, які необхідно формалізувати в подальших дослідженнях, алгоритмізувати та створити програмне забезпечення для їх підтримки. Здійснено класифікацію цих завдань, які необхідно вирішити у процесі створення системи інтелектуальної підтримки проєктних конкурсів. Для формального відбору проєктів пропонується використовувати схему послідовного аналізу варіантів множини проєктів на етапі попереднього аналізу. Сформований авторами комплекс завдань є передумовою для подальшої формалізації та розробки математичної моделі для задачі підтримки прийняття рішень під час організації та проведення конкурсів проєктів. Запропонована система забезпечить суттєве підвищення ефективності й об'єктивності проведення конкурсів проєктів.

Анотація (рус):

Анотація (англ):

Practical experience with project competitions shows that this is a complex multidimensional task. Competitions are usually characterised by a whole range of features that significantly affect their rules and technology, criteria and forms of evaluation. Objective evaluation of projects requires the involvement of a wide range of experts, which is not always possible. The use of artificial intelligence by humans is a key factor in achieving optimal results in many industries, from business to science and technology. The article is devoted to the problems of building an intelligent decision-making system for project competitions for a wide class of practical problem areas. The article provides an overview of scientific works in this area. The analysis of scientific publications has shown that there are several modes of application of artificial intelligence technologies in human activity, which determine the degree of its use. Based on the practical experience of developing and implementing intelligent systems gained by the authors of this paper, the main problems arising in the process of processing intelligent data are summarised, a number of tasks are identified, and solution schemes are proposed. The stages of competitions that require support by means of an intelligent decision support system are considered in detail. The authors provide a list of tasks that should be formalised in further research, algorithmised, and software should be created to support them. The authors classify these tasks to be solved when creating a system of intellectual support for project competitions. The authors propose to use a scheme of sequential analysis of the options for a set of projects at the stage of preliminary analysis for formal selection of projects. The set of tasks formed by the authors is a prerequisite for further formalisation and development of a mathematical model of the decision-making support task in organising and conducting project competitions The proposed system will ensure a significant increase in the efficiency and objectivity of project competitions.

Література:

1.     Schalkoff, R. J. (2009). Intelligent Systems: Principles, Paradigms, and Pragmatics: Principles, Paradigms, and Pragmatics. Jones & Bartlett Publishers.

2.     Zhu, S., Yu, T., Xu, T., Chen, H., Dustdar, S., Gigan, S., ... & Pan, Y. (2023). Intelligent computing: the latest advances, challenges, and future. Intelligent Computing, 2, 0006. https://doi.org/10.34133/icomputing.0006.

3.     Fedusenko, O., Domanetska, I., Lyashchenko, T. & Semeniuk, D. (2020). Training and gaming system for development of logic with intelligent interface. Management of Development of Complex Systems, 41, 133-140, dx.doi.org\10.32347/2412-9933.2020.41.133-140.

4.     Yankovy, I., Ilarionov, O., Krasovska, H. & Domanetska, I. (2021). Classifier of liver diseases according to textural statistics of ultrasound investigation and convolutional neural network. In CEUR Workshop Proceedings (pp. 60–69).

5.     Reuther, A., Michaleas, P., Jones, M., Gadepally, V., Samsi, S. & Kepner, J. (2022, September). AI and ML accelerator survey and trends. In 2022 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-10). IEEE.

6.     Ransbotham, S., Khodabandeh, S., Kiron, D., Candelon, F., Chu, M., LaFountain, B., … & Sarnoff, D. (2020). Expanding AI’s Impact With Organizational Learning. URL: https://sloanreview.mit.edu/projects/expanding-ais-impact-with-organizational-learning/

7.     Kim, Y. S., Ryoo, Y. J., Jang, M. S. & Bae, Y. C. (Eds.). (2014). Advanced Intelligent Systems (Vol. 268). Springer. https://doi.org/10.1007/978-3-319-05500-8.

8.     Dong, M., Bonnefon, J. F. & Rahwan, I. (2024). Toward human-centered AI management: Methodological challenges and future directions. Technovation, 131, 102953. https://doi.org/10.1016/j.technovation.2024.102953.

9.     Azofeifa, J. D., Noguez, J., Ruiz, S., Molina-Espinosa, J. M., Magana, A. J. & Benes, B. (2022). Systematic Review of Multimodal Human-Computer Interaction. Informatics 2022, 9, 13. https://doi.org/10.3390/informatics9010013

10.  Jarrahi, M. H., Askay, D., Eshraghi, A. & Smith, P. (2023). Artificial intelligence and knowledge management: A partnership between human and AI. Business Horizons, 66(1), 87-99. https://doi.org/10.1016/j.bushor.2022.03.002.

11.  Bhatt, U., Xiang, A., Sharma, S., Weller, A., Taly, A., Jia, Y., ... & Eckersley, P. (2020, January). Explainable machine learning in deployment. In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 648-657). https://doi.org/10.1145/3351095.3375624.

12.  Zirar, A., Ali, S. I. & Islam, N. (2023). Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda. Technovation, 124, 102747. https://doi.org/10.1016/j.technovation.2023.102747.

13.  Hnatiienko, H., Snytyuk, V., Tmienova, N., Zatonatskiy, D. & Zhylinska, O. (2022). Mathematical Support of the Task of Determining the Strategic Directions of Development and Priorities of the Organization. In IT&I (pp. 169-184).

14.  Hnatiienko, H., Hnatiienko, O., Tmienova, N. & Snytyuk, V. (2023). Mathematical Model of Management of the Corporate Culture of the Organizational System. CEUR Workshop Proceedings, 3624, 250–265.

15.  Hnatiienko, H., Snytyuk, V., Tmienova, N. & Voloshyn, O. (2021). Application of expert decision-making technologies for fair evaluation in testing problems. CEUR Workshop Proceedings, 2859, 46 – 60.

16.  Voloshin, A. F., Gnatienko, G. N. & Drobot, E. V. (2003). A Method of Indirect Determination of Intervals of Weight Coefficients of Parameters for Metricized Relations Between Objects. Journal of Automation and Information Sciences, 35, (1-4).

17.  Holzinger, A., Langs, G., Denk, H., Zatloukal, K. & Müller, H. (2019). Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(4), e1312.

18.  Islam, M. R., Ahmed, M. U., Barua, S. & Begum, S. (2022). A systematic review of explainable artificial intelligence in terms of different application domains and tasks. Applied Sciences, 12(3), 1353. https://doi.org/10.3390/app12031353.

19.  Bilan, S., Hnatiienko, V., Ilarionov, O. & Krasovska, H. (2023). Technology of Selection and Recognition of Information Objects on Images of the Earth's Surface Based on Multi-Projection Analysis. In IntSol (pp. 23-32).

20.  Ikenna, O. (2016). Project team building by the criterion of fulfillment (happiness): main problems and conceptual baselines. Upravlinnia proektamy ta rozvytok vyrobnytstva, 2(58), 110-124. http://pmdp.org.ua/index.php/en/2016/2-58-2016?id=1467.

21.  Rach, V., Osakwe, I., Medvedieva, O., Rossoshanska, O. & Borulko, N. (2019). Method for configuring the composition of a project team based on the criteria of subjective well-being. . Eastern-European Journal of Enterprise Technologies, 2 (3),
48–59. URL: http://journals.uran.ua/eejet/article/view/160651.

22.  Dolgikh, S. & Mulesa, O. (2021). Collaborative Human-AI Decision-Making Systems. CEUR Workshop Proceedings, 16130073.

23.  Mulesa, O., Povkhan, I., Radivilova, T. & Baranovskyi, O. (2021). Devising a method for constructing the optimal model of time series forecasting based on the principles of competition. Eastern-European Journal of Enterprise Technologies, 5 (4), 113. https://doi.org/10.15587/1729-4061.2021.240847.

24.  Hnatiienko, H. & Suprun, O. (2018, November). Fuzzy Set Objects Clustering Method Using Evolution Technologies. In ITS (pp. 129–138).

25.  Bimonte, S. (2016). Current approaches, challenges, and perspectives on spatial OLAP for agri-environmental analysis. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 7 (4), 32–49.

26.  Queiroz-Sousa, P. O. & Salgado, A. C. (2019). A review on OLAP technologies applied to information networks. ACM Transactions on Knowledge Discovery from Data (TKDD), 14(1), 1-25. http://doi.org/10.1145/3370912.

27.  Martens, D., Baesens, B. & Fawcett, T. (2011). Editorial survey: swarm intelligence for data mining. Machine Learning, 82, 1–42.

28.  Reddy, G. S. & Chittineni, S. (2021). Entropy based C4. 5-SHO algorithm with information gain optimization in data mining. PeerJ Computer Science, 7, e424.

29.  Stancu, M. S. & Duţescu, A. (2021). The impact of the Artificial Intelligence on the accounting profession, a literature’s assessment. In Proceedings of the International Conference on Business Excellence (Vol. 15, No. 1, pp. 749-758). Sciendo.

30.  Soori, M., Arezoo, B. & Dastres, R. (2023). Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics, 3, 54–70.

References:

  1. Schalkoff, R. J. (2009). Intelligent Systems: Principles, Paradigms, and Pragmatics: Principles, Paradigms, and Pragmatics. Jones & Bartlett Publishers.
  2. Zhu, S., Yu, T., Xu, T., Chen, H., Dustdar, S., Gigan, S., ... & Pan, Y. (2023). Intelligent computing: the latest advances, challenges, and future. Intelligent Computing, 2, 0006. https://doi.org/10.34133/icomputing.0006.
  3. Fedusenko, O., Domanetska, I., Lyashchenko, T. & Semeniuk, D. (2020). Training and gaming system for development of logic with intelligent interface. Management of Development of Complex Systems, 41, 133-140, dx.doi.org\10.32347/2412-9933.2020.41.133-140.
  4. Yankovy, I., Ilarionov, O., Krasovska, H. & Domanetska, I. (2021). Classifier of liver diseases according to textural statistics of ultrasound investigation and convolutional neural network. In CEUR Workshop Proceedings (pp. 60–69).
  5. Reuther, A., Michaleas, P., Jones, M., Gadepally, V., Samsi, S. & Kepner, J. (2022, September). AI and ML accelerator survey and trends. In 2022 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-10). IEEE.
  6. Ransbotham, S., Khodabandeh, S., Kiron, D., Candelon, F., Chu, M., LaFountain, B., … & Sarnoff, D. (2020). Expanding AI’s Impact With Organizational Learning. URL: https://sloanreview.mit.edu/projects/expanding-ais-impact-with-organizational-learning/
  7. Kim, Y. S., Ryoo, Y. J., Jang, M. S. & Bae, Y. C. (Eds.). (2014). Advanced Intelligent Systems (Vol. 268). Springer. https://doi.org/10.1007/978-3-319-05500-8.
  8. Dong, M., Bonnefon, J. F. & Rahwan, I. (2024). Toward human-centered AI management: Methodological challenges and future directions. Technovation, 131, 102953. https://doi.org/10.1016/j.technovation.2024.102953.
  9. Azofeifa, J. D., Noguez, J., Ruiz, S., Molina-Espinosa, J. M., Magana, A. J. & Benes, B. (2022). Systematic Review of Multimodal Human-Computer Interaction. Informatics 2022, 9, 13. https://doi.org/10.3390/informatics9010013
  10. Jarrahi, M. H., Askay, D., Eshraghi, A. & Smith, P. (2023). Artificial intelligence and knowledge management: A partnership between human and AI. Business Horizons, 66(1), 87-99. https://doi.org/10.1016/j.bushor.2022.03.002.
  11. Bhatt, U., Xiang, A., Sharma, S., Weller, A., Taly, A., Jia, Y., ... & Eckersley, P. (2020, January). Explainable machine learning in deployment. In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 648-657). https://doi.org/10.1145/3351095.3375624.
  12. Zirar, A., Ali, S. I. & Islam, N. (2023). Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda. Technovation, 124, 102747. https://doi.org/10.1016/j.technovation.2023.102747.
  13. Hnatiienko, H., Snytyuk, V., Tmienova, N., Zatonatskiy, D. & Zhylinska, O. (2022). Mathematical Support of the Task of Determining the Strategic Directions of Development and Priorities of the Organization. In IT&I (pp. 169-184).
  14. Hnatiienko, H., Hnatiienko, O., Tmienova, N. & Snytyuk, V. (2023). Mathematical Model of Management of the Corporate Culture of the Organizational System. CEUR Workshop Proceedings, 3624, 250–265.
  15. Hnatiienko, H., Snytyuk, V., Tmienova, N. & Voloshyn, O. (2021). Application of expert decision-making technologies for fair evaluation in testing problems. CEUR Workshop Proceedings, 2859, 46 – 60.
  16. Voloshin, A. F., Gnatienko, G. N. & Drobot, E. V. (2003). A Method of Indirect Determination of Intervals of Weight Coefficients of Parameters for Metricized Relations Between Objects. Journal of Automation and Information Sciences, 35, (1-4).
  17. Holzinger, A., Langs, G., Denk, H., Zatloukal, K. & Müller, H. (2019). Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(4), e1312.
  18. Islam, M. R., Ahmed, M. U., Barua, S. & Begum, S. (2022). A systematic review of explainable artificial intelligence in terms of different application domains and tasks. Applied Sciences, 12(3), 1353. https://doi.org/10.3390/app12031353.
  19. Bilan, S., Hnatiienko, V., Ilarionov, O. & Krasovska, H. (2023). Technology of Selection and Recognition of Information Objects on Images of the Earth's Surface Based on Multi-Projection Analysis. In IntSol (pp. 23-32).
  20. Ikenna, O. (2016). Project team building by the criterion of fulfillment (happiness): main problems and conceptual baselines. Upravlinnia proektamy ta rozvytok vyrobnytstva, 2(58), 110-124. http://pmdp.org.ua/index.php/en/2016/2-58-2016?id=1467.
  21. Rach, V., Osakwe, I., Medvedieva, O., Rossoshanska, O. & Borulko, N. (2019). Method for configuring the composition of a project team based on the criteria of subjective well-being. . Eastern-European Journal of Enterprise Technologies, 2 (3),
    48–59. URL: http://journals.uran.ua/eejet/article/view/160651.
  22. Dolgikh, S. & Mulesa, O. (2021). Collaborative Human-AI Decision-Making Systems. CEUR Workshop Proceedings, 16130073.
  23. Mulesa, O., Povkhan, I., Radivilova, T. & Baranovskyi, O. (2021). Devising a method for constructing the optimal model of time series forecasting based on the principles of competition. Eastern-European Journal of Enterprise Technologies, 5 (4), 113. https://doi.org/10.15587/1729-4061.2021.240847.
  24. Hnatiienko, H. & Suprun, O. (2018, November). Fuzzy Set Objects Clustering Method Using Evolution Technologies. In ITS (pp. 129–138).
  25. Bimonte, S. (2016). Current approaches, challenges, and perspectives on spatial OLAP for agri-environmental analysis. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 7 (4), 32–49.
  26. Queiroz-Sousa, P. O. & Salgado, A. C. (2019). A review on OLAP technologies applied to information networks. ACM Transactions on Knowledge Discovery from Data (TKDD), 14(1), 1-25. http://doi.org/10.1145/3370912.
  27. Martens, D., Baesens, B. & Fawcett, T. (2011). Editorial survey: swarm intelligence for data mining. Machine Learning, 82, 1–42.
  28. Reddy, G. S. & Chittineni, S. (2021). Entropy based C4. 5-SHO algorithm with information gain optimization in data mining. PeerJ Computer Science, 7, e424.
  29. Stancu, M. S. & Duţescu, A. (2021). The impact of the Artificial Intelligence on the accounting profession, a literature’s assessment. In Proceedings of the International Conference on Business Excellence (Vol. 15, No. 1, pp. 749-758). Sciendo.
  30. Soori, M., Arezoo, B. & Dastres, R. (2023). Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics, 3, 54–70.