Skip to main content
SpringerLink
Log in

Towards AGI: Cognitive Architecture Based on Hybrid and Bionic Principles

  • Conference paper
  • First Online:
Intelligent Computing

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 283))

Abstract

The article describes the author's proposal on cognitive architecture for the development of a general-level artificial intelligent agent («strong» artificial intelligence). New principles for the development of such an architecture are proposed—a hybrid approach in artificial intelligence and bionics. The architecture diagram of the proposed solution is given and descriptions of possible areas of application are described. Strong artificial intelligence is a technical solution that can solve arbitrary cognitive tasks available to humans (human-level artificial intelligence) and even surpass the capabilities of human intelligence (artificial superintelligence). The fields of application of strong artificial intelligence are limitless—from solving current problems facing the human to completely new problems that are not yet available to human civilization or are still waiting for their discoverer. The novelty of the work lies in the author's approach to the construction of cognitive architecture, which has absorbed the results of many years of research in the field of artificial intelligence and the results of the analysis of cognitive architectures of other researchers. The relevance of the work is based on the indisputable fact that current research in the field of weak artificial intelligence is starting to slow down due to the impossibility of solving general problems, and most national strategies for the development of technologies in the field of artificial intelligence declare the need to develop new artificial intelligence technologies, including Artificial General Intelligence. The work will be of interest to scientists, engineers, and researchers working in the field of artificial intelligence in general, as well as to any interested readers seeking to keep abreast of modern technologies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dushkin, R.V.: Artificial intelligence. DMK-Press, Moscow (2019). 280 p., ISBN 978-5-97060-787-9

    Google Scholar 

  2. Pargman, D., Biørn-Hansen, A., Eriksson, E., Laaksolahti, J., Robèrt, M.: From moore's law to the carbon law. In: ICT4S2020: 7th International Conference on ICT for Sustainability, pp. 285–293, June 2020. https://doi.org/10.1145/3401335.3401825

  3. Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd ed. Prentice-Hall, Upper Saddle River (2003). ISBN 0-13-790395-2

    Google Scholar 

  4. Ghallab, M., Ingrand, F.: Robotics and artificial intelligence. In: Marquis, P., Papini, O., Prade, H. (eds.) A Guided Tour of Artificial Intelligence Research, pp. 389–436. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-06170-8_12

    Chapter  Google Scholar 

  5. Mokhov, A.I., Dushkin, R.V., Andronov, M.G., Maltsev, V.P.: Methodology for assessing the degree of intelligence of technical and sociotechnical systems. Digit. Econ. (2019). http://bit.ly/2of6hhg

  6. Searle, J.: Minds, brains, and programs. Behav. Brain Sci. 3(3), 417 (1980). https://doi.org/10.1017/S0140525X00005756.

  7. Ng, G.-W., Leung, W.: Strong artificial intelligence and consciousness. J. Artif. Intell. Consciousness. 07, 63–72 (2020). https://doi.org/10.1142/S2705078520300042

  8. Yates, S., Walker, A., Kerri, V.M.: Artificial Intelligence (2020)

    Google Scholar 

  9. Lipov, A.N.: At the origins of modern bionics. Bio-morphological formation in an artificial environment. Polygnosis. № 1–2, Ch. 1–2, pp. 126–136 (2010)

    Google Scholar 

  10. Dushkin, R.V., Onatsik, D.A., Sutsilov, D.S., Fadeeva, S.: Analytical report «Comparison of national strategies in the field of artificial intelligence». Issue No. 2/2019. Moscow: Deecrypto LLC, Artificial Intelligence Agency, (2019). 16 p.

    Google Scholar 

  11. Dushkin, R.V., Andronov, M.G.: The hybrid design for artificial intelligence systems. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2020. AISC, vol. 1250, pp. 164–170. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-55180-3_13

    Chapter  Google Scholar 

  12. Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press (2007). ISBN 978-0-521-71770-0

    Google Scholar 

  13. Kordon, A.: Problem knowledge acquisition. In: Applying Data Science, How to Create Value with Artificial Intelligence, September 2020. https://doi.org/10.1007/978-3-030-36375-8_7.

  14. Flasiński, M.: Symbolic artificial intelligence. In: Introduction to Artificial Intelligence, pp. 15–22. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40022-8_2

  15. Tahmasebi, H.: A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Comput. Geosci. 42, 18–27 (2012). Bibcode:2012CG.....42...18T. https://doi.org/10.1016/j.cageo.2012.02.004

  16. Dushkin, R.V.: On the issue of recognition and differentiation of the philosophical zombie. Philosophical thought (1), 52–66 (2020). https://doi.org/10.25136/2409-8728.2020.1.32079

  17. Kranowitz, C.S., Silver, L.B.: The Out-of-Sync Child. Penguin Books (2006). 352 p. ISBN 978-0-39953-271-9

    Google Scholar 

  18. Harnad, S.: The Symbol Grounding Problem. Physica D 42, 335–346 (1990). https://clck.ru/RD7qu. Accessed 04 Oct 2020

  19. Shumsky, S.A.: Machine intelligence. Essays on the theory of machine learning and artificial intelligence. RIOR, Moscow (2020). 340 p. ISBN 978-5-369-01832-3

    Google Scholar 

  20. Zalta, E.N.: Gottlob frege. In: Zalta, E.N. (ed.) Stanford Encyclopedia of Philosophy (Fall 2014) (2014)

    Google Scholar 

  21. Osipov, G.S.: Signs-based vs. symbolic models. In: Advances in Artificial Intelligence and Soft Computing (2015)

    Google Scholar 

  22. Glazunov, Yu.T.: emotional experience in the human goal-setting system. MSTU Bull. 14(1), 126–140 (2011)

    Google Scholar 

  23. Sundas, A., Bhatia, A., Saggi, M., Ashta, J.: Reinforcement learning. In: Machine Learning and Big Data: Concepts, Algorithms, Tools, and Applications. Wiley, July 2020

    Google Scholar 

  24. Hawkins, J., Blakeslee, S.: On Intelligence. Owl Books, New York (2005). ISBN 978-0-8050-7853-4

    Google Scholar 

  25. Kurzweil, R.: How to Create a Mind: The Secret of Human Thought Revealed. Viking Books, New York (2012). ISBN 978-0-670-02529-9

    Google Scholar 

  26. Anokhin, P.K.: Essays on the physiology of functional systems (1975)

    Google Scholar 

  27. Georgakopoulos, T.: Semantic maps. In: Oxford Bibliographies in LinguisticsPublisher. Oxford University Press, New York (2019). https://doi.org/10.1093/obo/9780199772810-0229

  28. Bibel, W., Kurfeß, F., Aspetsberger, K., Hintenaus, P., Schumann, J.: Parallel inference machines. In: Treleaven, P., Vanneschi, M. (eds.) Future Parallel Computers. LNCS, vol. 272, pp. 185–226. Springer, Heidelberg (1987). https://doi.org/10.1007/3-540-18203-9_5

    Chapter  Google Scholar 

  29. Liu, Zh., Lin, Y., Sun, M.: Representation Learning and NLP. In: Representation Learning for Natural Language Processing, July 2020. https://doi.org/10.1007/978-981-15-5573-2_1

Download references

Author information

Authors and Affiliations

  1. Artificial Intelligence Agency, 1st Volkonsky Lane. 15, 127473, Moscow, Russian Federation

    R. V. Dushkin

Authors
  1. R. V. Dushkin
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dushkin, R.V. (2022). Towards AGI: Cognitive Architecture Based on Hybrid and Bionic Principles. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-80119-9_19

Download citation

Publish with us

Policies and ethics

Search

Navigation