1. SISN
  2. All Volumes and Issues
  3. Volume 10, 2021
  4. Analysis of Artificial Intellectual Agents for E-Learning Systems

Analysis of Artificial Intellectual Agents for E-Learning Systems

SISN.
2021;
Volume 10
: pp. 41 - 57
https://doi.org/10.23939/sisn2021.10.041
Authors:
  1. Valentyna Yunchyk
  2. Natalia Kunanets
  3. Volodymyr Pasichnyk
  4. Anatolii Fedonyuk
1
Lesya Ukrainka Volyn National University
2
Lviv Politechnik National University, Department of Information Systems and Networks
3
Lviv Politechnik National University
4
Lesya Ukrainka Volyn National University

The key terms and basic concepts of the agent are analyzed. The structured general classification of agents according to the representation of the model of the external environment, by the type of processing information and by the functions performed is given. The classification of artificial agents (intellectual, reflex, impulsive, trophic) also is s analyzed. The necessary conditions for the implementation of a certain behavior by the agent are given, as well as the scheme of functioning of the intelligent agent. The levels of knowledge that play a key role in the architecture of the agent are indicated. The functional diagram of a learning agent that works relatively independently, demonstrating flexible behavior. It is discussed that the functional scheme of the reactive agent determines the dependence on the environment. The properties of the intelligent agent are described in detail and the block diagram is indicated. Various variants of agent architectures, in particular neural network agent architectures, are considered. The organization of level interaction in the multilevel agent architecture is proposed. Considerable attention is paid to the Will-architecture and InteRRaP- architecture of agents. A multilevel architecture for an autonomous agent of a Turing machine is considered.

agent
intellectual agent
classification of agents
multilevel architecture
  1. Barbuceanu M., Fox M. (1996). Capturing and Modeling Coordination Knowledge for Multi-Agent Systems. International Journal on Intelligent and Cooperative Information Systems, 05 (02n03), 275–314. URL: https://doi.org/10.1142/S0218843096000117.
  2. Karthik D. (2011). Suituation based intelligence routing in wireless sensor network. International Conference on Computational Intelligence and Computing Research. URL: http://www.itfrindia.org/ICCIC/ Vol2/447ICCIC.pdf.
  3. Khomyak M., Fedonuyk A., Yatsyuk S, Yunchyk V. (2021). Cognitive modeling of the learning process of training IT specialists. 3rd International Workshop on Modern Machine Learning Technologies and Data Science CEUR Workshop Proceedings, 2917, 141–150. URL: http://ceur-ws.org/Vol-2917/paper13.pdf.
  4. Hayes-Roth B. (1995). An Architecture for Adaptive Intelligent Systems. Artificial Intelligence. 72, 329— 365. https://doi.org/10.1016/0004-3702(94)00004-K.
  5. Maes P. (1995). Artificial Life Meets Entertainment: Life Like Autonomous Agents. Communication of the ACM, 38(11), 108–114. https://doi.org/10.1145/219717.219808.
  6. Fedonuyk A., Yunchyk V., Yatsyuk S.,Cheprasova T. (2020) The Models of Data and Knowledge Representation in Educational System of Mathematical Training of IT-specialists. IEEE 15th International Scientific and Technical Conference on Computer Sciences and Information Technologies, 2, 269–272. DOI: 10.1109/CSIT49958.2020.9321899.
  7. Boyko R. O., Uk D. D. (2015). The concept and properties of the agent in multi-agent information systems. Modern methods, information, software and technical support of management systems of organizational, technical and technological complexes: materials of the II International scientific and technical conference, 136–137. URL: http://dspace.nuft.edu.ua/jspui/handle/123456789/23854.
  8. Vovnyanka R. V. Methods and means of action planning of specialized intellectual agents on the basis of the ontological approach: the diss. ...cand. of techn. sciences: 01.05.03 — mathematical and software of computers and systems. Lviv, 230. URL: https://lpnu.ua/sites/default/files/2020/dissertation/1431/dysvovniankar...
  9. Gavrilova T. A., Khoroshevsky V. F. (2000). Knowledge base of intelligent systems. SPb: Peter, 384.
  10. Galushkin A. I. (2000). Theory of neural networks: textbook for universities. Moscow: Radiotekhnika, 415.
  11. Gorodetsky V. I. (1996). Multi-agent systems: current state of research and application prospects. Artificial Intelligence News, 1, 44–59. URL: http://raai.org/library/ainews/getainews.php?1996
  12. Gorokhov A. V. (2019). Fundamentals of systems analysis: a textbook for universities. Moscow: Yurayt Publishing House, 140.
  13. Kandrashina E. Yu., Litvintseva L.V., Pospelov D. A. (1988). Representation of knowledge about space and time in artificial intelligence systems. Moscow: Nauka, 328.
  14. Kitsun G. V. (2006). The architecture of the intellectual agent. Bulletin of the Lviv Politechnic National University, 573, 96–103. URL: http://ena.lp.edu.ua:8080/handle/ntb/30105
  15. Lisiev G. A., Popova I. V., Lisiev G. A. (2017). Decision support technologies: a tutorial. Moscow: Flint Publishing House, 133.
  16. Lozinsky A. Ya., Teslyuk V. M., Zelinsky A. Ya., Narushinska O. O. (2017). Analysis of modern multi- agent systems. Model and Information Technologies, 81, 156–166. URL: http://nbuv.gov.ua/UJRN/Mtit_2017_81_24
  17. Narozhny A. V. (2013). Agent-based approach to building management systems for the learning process. Eastern European Journal of Advanced Technologies, 5/3 (65), 20–23. DOI: https://doi.org/10.15587/1729-4061.2013.18478.
  18. Russell Stewart, Norvig Peter. (2007). Artificial Intelligence: A Modern Approach. Per. from English. Moscow: Williams Publishing House, 1408.
  19. Romanov V.P. (2003). Intelligent information systems in economics: A textbook. Moscow: Publishing House “Exam”, 496.
  20. Savenko O., Krishchuk A., Lysenko S. (2011). Diagnosing computer systems for malware based on an antivirus multiagent system. Bulletin of the Lviv Polytechnic National University, 717, 147–152. URL: http://ena.lp.edu.ua:8080/handle/ntb/12229.
  21. Samodurova D. A. (2019). Intelligent agents and multiagent systems in production. Economic Bulletin of Donbass, 2 (56), 179–186. DOI: 10.12958/1817-3772-2019-2(56)-179-186.
  22. Simonenko O. A., Umanets Ya. L., Romanyuk V. A., Sova O. Ya. (2013). An analysis of the capabilities of the network of intellectual agents to induce the system and management of the radio stations of the radio station to the MANET class. Collection of Science Practitioners VІТІ NTUU “KPI”, 1. URL: http://www.viti.edu.ua/files/zbk/2013/10_1_2013.pdf.
  23. Tarasov V.B. (1998). Agents, multi-agent systems, virtual communities: a strategic direction in computer science and artificial intelligence. Artificial Intelligence News, 3, 5–54. URL: http://masters.donntu.org/2009/fvti/zaytsev/library/book8/
  24. Tarasov V. B. (2002). From multi-agent systems to intelligent organizations: philosophy, psychology, computer science. Moscow: Editorial URSS, 352.
  25. Fedoruk P. І. (2004). Victory of intellectual agents for the intensification of the process of initiation. Arts, intelligence, 3, 379–384. URL: http://www.iai.dn.ua/public/JournalAI_2004_3/Razdel4/16_Fedoruk_.pdf.
  26. Fedyaev O. I., Zhabskaya T. E., Grach E. G. (2006). Multi-agent model of the process of teaching students at the department level. Science of the Donetsk National Technical University. Series “Problems of modeling and automation of design of dynamical systems”, 5 (116), 105–116. URL: http://ea.donntu.edu.ua/bitstream/123456789/5815/1/11.pdf.
  27. Shvetsov A. N., Rzheutskaya S. Yu., Sergushicheva A. P., Sukonshchikov A. A. Architecture of an intelligent agent-based educational complex for training specialists of a technical profile. Open Education, 22 (3), 14— 24. DOI: http://dx.doi.org/10.21686/1818-4243-2018-3-14-24.