Open Access Peer-reviewed Review

Robotics by multimodal self-organizing ensembles of software and hardware agents with artificial intelligence

Main Article Content

Evgeniy Bryndin corresponding author

Abstract

Self-organizing ensembles of software and hardware agents with artificial intelligence model the intellectual abilities of a person's natural intelligence. The Creator endowed man with various types of intellectual abilities: generation of meanings, perception of meanings, meaningful actions and behavior, sensory reaction to meanings, emotional reaction to meanings. Based on the synergy of various intellectual abilities, a person carries out life activities. For example, Dialogue is conducted on the basis of two intellectual abilities: the generation and perception of meanings. A multimodal self-organizing ensemble of intelligent software and hardware agents with artificial intelligence, based on existing knowledge and skills, is able to write poetry, draw pictures, give recommendations and solutions to specialists, manage production and systems in various sectors of the economy, and take part in scientific research. Multimodal ensembles of intelligent agents, modeling the functions of natural intelligence, contain a functional control structure. To ensure the safe and reliable use of multimodal ensembles of intelligent agents, they are being standardized internationally under the guidance of ISO. International standardization of multimodal ensembles of intelligent agents expands the market and reduces the risks of their use.

Keywords
multimodal self-organizing ensembles, software and hardware agents, artificial intelligence

Article Details

How to Cite
Bryndin, E. (2024). Robotics by multimodal self-organizing ensembles of software and hardware agents with artificial intelligence. Research on Intelligent Manufacturing and Assembly, 2(1), 60-69. https://doi.org/10.25082/RIMA.2023.01.003

References

  1. Chapkin NS. Artificial intelligence and prospects for its development. Culture of Peace. 2023, 11(4): 69-78.
  2. Williams A. Human-Centric Functional Computing as an Approach to Human-Like Computation. Artificial Intelligence and Applications. 2022, 1(2): 128-137. https://doi.org/10.47852/bonviewaia2202331
  3. Li Y, Zhang S, Zhang X. Architectural AI Design Based on Fused Text and Image Data in the Context of a Carbon-Silicon World. Journal of Research in Social Science and Humanities. 2023, 2(9): 45-50. https://doi.org/10.56397/jrssh.2023.09.07
  4. Bryndin E. Cryptos Control of Humanoid Intelligent Digital Twin Based on Spectral and Holographic Approaches. Journal of Progress in Engineering and Physical Science. 2023, 2(3): 1-10. https://doi.org/10.56397/jpeps.2023.09.01
  5. Bryndin E. Increased Sensitivity and Safety of Cognitive Robot by Developing Professional and Behavioral Skills. Saudi Journal of Engineering and Technology. 2020, 05(05): 187-196. https://doi.org/10.36348/sjet.2020.v05i05.001
  6. Bryndin E. Cognitive Resonant Communication by Internal Speech Through Intelligent Bioinformation Systems. Budapest International Research in Exact Sciences (BirEx) Journal. 2023, 5(4): 223-234.
  7. Bryndin E. Ensembles of Intelligent Agents with Expanding Communication Abilities. Acta Scientific Computer Sciences. 2023, 5(2): 44-49.
  8. ISO/IEC JTC 1/SC 42/WG 4 Use cases and applications Convenorship: JISC (Japan). 2019-12-23. https://isotc.iso.org
  9. Bryndin EG. Standardization of artificial intelligence. Standards and Quality. 2020, 12: 22-25.
  10. Bryndin Russia E. Standardization of Artificial Intelligence for the Development and Use of Intelligent Systems. Advances in Wireless Communications and Networks. 2020, 6(1): 1. https://doi.org/10.11648/j.awcn.20200601.11
  11. Kounta CAKA, Kamsu-Foguem B, Noureddine F, et al. Multimodal deep learning for predicting the choice of cut parameters in the milling process. Intelligent Systems with Applications. 2022, 16: 200112. https://doi.org/10.1016/j.iswa.2022.200112
  12. Liu S, Gao P, Li Y, et al. Multi-modal fusion network with complementarity and importance for emotion recognition. Information Sciences. 2023, 619: 679-694. https://doi.org/10.1016/j.ins.2022.11.076
  13. Dai Y, Yan Z, Cheng J, et al. Analysis of multimodal data fusion from an information theory perspective. Information Sciences. 2023, 623: 164-183. https://doi.org/10.1016/j.ins.2022.12.014
  14. Bryndin E. Formation of International Ethical Digital Environment with Smart Artificial Intelligence. Automation, Control and Intelligent Systems. 2021, 9(1): 22. https://doi.org/10.11648/j.acis.20210901.14
  15. Bryndin E. Implementation of Competencies by Smart Ethical Artificial Intelligence in Different Environments. Software Engineering. 2020, 8(4): 24. https://doi.org/10.11648/j.se.20200804.11
  16. Bryndin E. Development of Artificial Intelligence for Library Activity and Industrial and Social Robotization. Chapter of book ``Application of Artificial Intelligence in Library Services''. Springer. 2024.
  17. Bryndin EG. Development of artificial intelligence of ensembles of software and hardware agents to natural intelligence based on self-organization. Yearbook ``Greater Eurasia: development, security, cooperation.'' 2024, 7(2): 42-49.
  18. Weber-Lewerenz B, Traverso M. Navigating Applied Artificial Intelligence (AI) in the Digital Era: How Smart Buildings and Smart Cities Become the Key to Sustainability. Artificial Intelligence and Applications. 2023, 1(4): 230-243. https://doi.org/10.47852/bonviewaia32021063
  19. OTUS Blog, Artificial intelligence. How will artificial intelligence be regulated in 2024? https://habr.com/ru/companies/otus/articles/787498
  20. Kolin KK. The information paradigm of cognition and a new worldview. Information processes, systems and technologies. 2023, 4(4): 5-18.
  21. Kolin K. The new information reality and the problem of the formation of the scientific branch ``Information Sciences''. International Journal of Open Information Technologies. 2024, 12(1): 137-143.
  22. Katkade SN, Bagal VC, Manza RR, et al. Advances in Real-Time Object Detection and Information Retrieval: A Review. Artificial Intelligence and Applications. 2023, 1(3): 139-144. https://doi.org/10.47852/bonviewaia3202456
  23. Salem RB, Aimeur E, Hage H. A Multi-Party Agent for Privacy Preference Elicitation. Artificial Intelligence and Applications. 2022, 1(2): 98-105. https://doi.org/10.47852/bonviewaia2202514
  24. Luo C. KELL: A Kernel-Embedded Local Learning for Data-Intensive Modeling. Artificial Intelligence and Applications. 2023, 2(1): 38-44. https://doi.org/10.47852/bonviewaia32021381
  25. Teófilo-Salvador E, Ambrocio-Cruz P, Rosado-Solís M. Methodological Characterization and Computational Codes in the Simulation of Interacting Galaxies. Artificial Intelligence and Applications. 2023, 2(1): 45-58. https://doi.org/10.47852/bonviewaia3202743