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Human Digital Doubles with Technological Cognitive Thinking and Adaptive Behaviour

Received: 4 June 2019    Accepted: 3 July 2019    Published: 13 July 2019
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Abstract

Transdisciplinary paradigm of digital researches, acting as the basis of synthesis of knowledge of the person, the nature, society and production, supplements scientific rationality. She allows to create digital doubles of social services and production of production of products and technological process of operation of the equipment. Digital doubles by training of neural network systems on the basis of the saved-up big data relating to services sector or production for intellectual management of technological processes and the equipment are created. The digital double carries out activity automatically on the instructions of the person. Intellectual production management by digital doubles optimizes its work, increases labor productivity and competitiveness of products on quality and the price. Digital doubles of the person render services in the social sphere. Specialization of digital doubles to professional competences is carried out on the basis of communicative associative logic of technological thinking by cognitive methods. The international scientific and engineering society gradually moves to technical realization of the cognitive professional robot with retraining. The group of the University of California in Berkeley and Princeton investigating efficiency of methods of machine learning for forecasting of human behavior offered the new approach which is given rise on a joint of AI and cognitive psychology. Scientists presented the new concept providing preliminary training of neural networks at the synthetic data prepared by psychologists by means of the existing theoretical models. Approach can be used by other groups for a training of their own models of machine learning. Approach combines the existing scientific theories of behavior of the person with flexibility of neural networks for the best forecasting of the risky decisions made by the person. From the practical point of view it allows to save to researchers a lot of time which is spent usually on data collection for the knowledge base of human behavior.

Published in Software Engineering (Volume 7, Issue 1)
DOI 10.11648/j.se.20190701.11
Page(s) 1-9
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Technological Thinking, Communicative and Associative Logic, Digital Doubles, Communicative Associative Logic, Adaptive Behavior

References
[1] Evgeniy G. Bryndin The Robot with imitative thinking. "PNIPU bulletin: Electrical equipment, Information technologies, Control systems", N. 14. Perm: PNIPU. Pages 5-36. 2015.
[2] David Vernon. Artificial Cognitive Systems. MIT Press, Cambridge, Massachusetts. London, England. 2014. 70 pages.
[3] Hooman Samani. Cognitive Robotics. CRC Press. 2015. 220 Pages.
[4] Evgeniy G. Bryndin, Cognitive robot consultant for a healthy lifestyle. III International scientific conference "Information Technologies in Science, Management, the Social Sphere and Medicine". TPU. Pages 484-488. 2016.
[5] Bryndin E. G. Cognitive robots. Inter. Conf. "Management of development of large-scale systems (MLSD’2016). M.: IPM RAS. Pages 285-294. 2016.
[6] Evgeniy Bryndin. Cognitive Robots with Imitative Thinking for Digital Libraries, Banks, Universities and Smart Factories. International Journal of Management and Fuzzy Systems. V. 3, N. 5, 2017, pp 57-66.
[7] Evgeniy Bryndin. Technological Thinking, Communication and Behavior of Androids. Communications. Vol. 6, No. 1, 2018. Pages: 13-19.
[8] Stefan Wermter, Günther Palm, Mark Elshaw. Biomimetic Neural Learning for Intelligent Robots: Intelligent Systems, Cognitive Robotics, and Neuroscience. Springer-Verlag Berlin Heidelberg. 2005. 389 pages.
[9] Ahmed S. Al-Araji, M. Sc. Khulood E. Dagher. Cognitive Neural Controller for Mobile Robot. IJCCCE Vol. 15, No. 1, 2015. P. 46-60.
[10] Evgeniy Bryndin. Program Hierarchical Realization of Adaptation Behavior of the Cognitive Mobile Robot with Imitative Thinking. International Journal of Engineering Management. Volume 1, Issue 4. 2017, pp. 74-79.
[11] Evgeniy Bryndin. Technological, Economic and Social Aspects of Management by Development of the Digital Industry 4.0. International Journal of Managerial Studies and Research (IJMSR), vol 6, no. 4, 2018, pp. 19-30.
[12] Evgeniy Bryndin. Directions of Development of Industry 4.0, Digital Technology and Social Economy. American Journal of Information Science and Technology. V 2, Issue 1. 2018. P. 9-17.
[13] Evgeniy Bryndin. Social Cognitive Smart Robots: Guide, Seller, Lecturer, Vacuum Cleaner, Nurse, Volunteer, Security Guard, Administrator. Communications. Volume 7, Issue 1. 2019. Pages: 6-12.
[14] Evgeniy Bryndin. Cognitive smart robots with technological thinking and behavior for industry and social sphere. /Science, Technology and Life: Proceedings of articles the V International scientific conference. - Czech Republic, Karlovy Vary. 2019. ISBN 978-80-7534-195-2.
[15] Evgeniy Bryndin. Digital technologies of the industry 4.0 /Chepter 10, С. 201-222, Book: Computer Science Advances: Research and Applications. USA: Nova Science Publisher. 2019. 252pages.
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  • APA Style

    Evgeniy Bryndin. (2019). Human Digital Doubles with Technological Cognitive Thinking and Adaptive Behaviour. Software Engineering, 7(1), 1-9. https://doi.org/10.11648/j.se.20190701.11

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    ACS Style

    Evgeniy Bryndin. Human Digital Doubles with Technological Cognitive Thinking and Adaptive Behaviour. Softw. Eng. 2019, 7(1), 1-9. doi: 10.11648/j.se.20190701.11

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    AMA Style

    Evgeniy Bryndin. Human Digital Doubles with Technological Cognitive Thinking and Adaptive Behaviour. Softw Eng. 2019;7(1):1-9. doi: 10.11648/j.se.20190701.11

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  • @article{10.11648/j.se.20190701.11,
      author = {Evgeniy Bryndin},
      title = {Human Digital Doubles with Technological Cognitive Thinking and Adaptive Behaviour},
      journal = {Software Engineering},
      volume = {7},
      number = {1},
      pages = {1-9},
      doi = {10.11648/j.se.20190701.11},
      url = {https://doi.org/10.11648/j.se.20190701.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.se.20190701.11},
      abstract = {Transdisciplinary paradigm of digital researches, acting as the basis of synthesis of knowledge of the person, the nature, society and production, supplements scientific rationality. She allows to create digital doubles of social services and production of production of products and technological process of operation of the equipment. Digital doubles by training of neural network systems on the basis of the saved-up big data relating to services sector or production for intellectual management of technological processes and the equipment are created. The digital double carries out activity automatically on the instructions of the person. Intellectual production management by digital doubles optimizes its work, increases labor productivity and competitiveness of products on quality and the price. Digital doubles of the person render services in the social sphere. Specialization of digital doubles to professional competences is carried out on the basis of communicative associative logic of technological thinking by cognitive methods. The international scientific and engineering society gradually moves to technical realization of the cognitive professional robot with retraining. The group of the University of California in Berkeley and Princeton investigating efficiency of methods of machine learning for forecasting of human behavior offered the new approach which is given rise on a joint of AI and cognitive psychology. Scientists presented the new concept providing preliminary training of neural networks at the synthetic data prepared by psychologists by means of the existing theoretical models. Approach can be used by other groups for a training of their own models of machine learning. Approach combines the existing scientific theories of behavior of the person with flexibility of neural networks for the best forecasting of the risky decisions made by the person. From the practical point of view it allows to save to researchers a lot of time which is spent usually on data collection for the knowledge base of human behavior.},
     year = {2019}
    }
    

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    AB  - Transdisciplinary paradigm of digital researches, acting as the basis of synthesis of knowledge of the person, the nature, society and production, supplements scientific rationality. She allows to create digital doubles of social services and production of production of products and technological process of operation of the equipment. Digital doubles by training of neural network systems on the basis of the saved-up big data relating to services sector or production for intellectual management of technological processes and the equipment are created. The digital double carries out activity automatically on the instructions of the person. Intellectual production management by digital doubles optimizes its work, increases labor productivity and competitiveness of products on quality and the price. Digital doubles of the person render services in the social sphere. Specialization of digital doubles to professional competences is carried out on the basis of communicative associative logic of technological thinking by cognitive methods. The international scientific and engineering society gradually moves to technical realization of the cognitive professional robot with retraining. The group of the University of California in Berkeley and Princeton investigating efficiency of methods of machine learning for forecasting of human behavior offered the new approach which is given rise on a joint of AI and cognitive psychology. Scientists presented the new concept providing preliminary training of neural networks at the synthetic data prepared by psychologists by means of the existing theoretical models. Approach can be used by other groups for a training of their own models of machine learning. Approach combines the existing scientific theories of behavior of the person with flexibility of neural networks for the best forecasting of the risky decisions made by the person. From the practical point of view it allows to save to researchers a lot of time which is spent usually on data collection for the knowledge base of human behavior.
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Author Information
  • Scientific Department, Research Center "Estestvoinformatika", Novosibirsk, Russia

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