Abstract
The article describes the author’s approach to solving the problem of symbol grounding, which can be used in the development of artificial cognitive agents of the general level. When implementing this approach, such agents can receive the function of understanding the sense and context of the situations in which they find themselves. The article gives a brief description of the problem of understanding the meaning and sense. In addition, the author’s vision is given of how the symbol grounding should occur when the artificial cognitive agent uses sensory information flows of various modality. Symbol grounding is carried out by building an associative-heterarchical network of concepts, with the help of which the hybrid architecture of an artificial cognitive agent is expanded. The novelty of the article is based on the author’s approach to solving the problem, which is represented by several important principles—these are multisensory integration, the use of an associative-heterarchical network of concepts and a hybrid paradigm of artificial intelligence. The relevance of the work is based on the fact that today the problem of constructing artificial cognitive agents of a general level is becoming more and more important for solving, including within the framework of national strategies for the development of artificial intelligence in various countries of the world. The article is of a theoretical nature and will be of interest to specialists in the field of artificial intelligence, as well as to all those who want to stay within the framework of modern trends in the field of artificial intelligence.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Harnad, S.: The symbol grounding problem. Phys. D: Nonlinear Phenom. 42(1–3), 335–346 (1990). https://doi.org/10.1016/0167-2789(90)90087-6
Frege, F.L.G.: Über sinn und bedeutung. Zeitschrift für Philosophie und Philosophische Kritik 25–50 (1892)
Wittgenstein, L.: Logical and philosophical treatise. Translation from German by Dobronravova and Lakhuti, pp. 133. Common ed. and foreword by Asmus V.F. Nauka, Moscow 1958 (2009) (1958)
Zalta, E.N.: Gottlob frege. In: Zalta, E.N. (ed.) Stanford Encyclopedia of Philosophy (Fall 2014) (2014)
Osipov, G.S.: Signs-based vs. symbolic models. Advances in Artificial Intelligence and Soft Computing (2015)
Panov, A.I., Petrov, A.V.: Hierarchical temporary memory as a model of perception and its automatic representation. In: Sixth International Conference “System Analysis and Information Technologies” SAIT-2015 (June 15–20, 2015, Svetlogorsk, Russia): Proceedings of the conference, vol. 2 (2015)
Dushkin, R.V.: On j. searle’s “chinese room” from the hybrid model of the artificial cognitive agents design. Sib. J. Philos. 18(2), 30–47 (2020). https://doi.org/10.25205/2541-7517-2020-18-2-30-47
Masse, A., Chicoisne, G., Gargouri, Y., Harnad, S., Picard, O., Marcotte, O.: How is meaning grounded in dictionary definitions? (2008). https://doi.org/10.3115/1627328.1627331
Dushkin, R.V.: Is it possible to recognize a philosophical zombie and how to do it. In: Arai, K. (ed.) Intelligent Systems and Applications IntelliSys 2021 Proceedings of the 2021 Intelligent Systems Conference (IntelliSys) Volume 1. LNNS, vol. 294, pp. 778–790. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-82193-7_52
Stepankov, V.Y., Dushkin, R.V.: Hierarchical associative memory model for artificial general-purpose cognitive agents. Procedia Comput. Sci. 190, 723–727 (2021). https://doi.org/10.1016/j.procs.2021.06.084
Stout, D., Khreisheh, N.: Skill learning and human brain evolution: an experimental approach. Camb. Archaeol. J. 25(4), 867–875 (2015). https://doi.org/10.1017/S0959774315000359
Leshchev, S.V.: From artificial intelligence to dissipative sociotechnical rationality: cyberphysical and sociocultural matrices of the digital age. In: Popkova, E.G., Ostrovskaya, V.N., Bogoviz, A.V. (eds.) Socio-economic Systems: Paradigms for the Future. SSDC, vol. 314, pp. 65–72. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-56433-9_8
Shumsky, S.A.: Machine intelligence. Essays on the Theory of Machine Learning and Artificial Intelligence, pp. 340. RIOR Publ., Moscow (2020). ISBN: 978-5-369-01832-3
Sundas, A., Bhatia, A., Saggi, M., Ashta, J.: Reinforcement learning. In book: Machine Learning and Big Data: Concepts, Algorithms, Tools, and Applications. John Wiley & sons, July 2020 (2020)
LeDoux, J.E.: How does the non-conscious become conscious? Curr. Biol. 30(5), R196–R199 (2020). https://doi.org/10.1016/j.cub.2020.01.033
Harnad, S.: To cognize is to categorize: cognition is categorization. In: Handbook of Categorization in Cognitive Science, pp. 19–43. Elsevier (2005). https://doi.org/10.1016/B978-008044612-7/50056-1
Dushkin, R.V., Stepankov, V.Y.: hybrid bionic cognitive architecture for artificial general intelligence agents. Procedia Comput. Sci. 190, 226–230 (2021). https://doi.org/10.1016/j.procs.2021.06.028
Dushkin, R.V., Stepankov, V.Y.: Semantic supervised training for general artificial cognitive agents. In: Tallón-Ballesteros, A.J. (ed.) Fuzzy Systems and Data Mining VII: Proceedings of FSDM 2021. IOS Press (2021). https://doi.org/10.3233/FAIA210215
Žáček, M., Telnarová, Z.: Language networks and semantic networks. Central European Symposium on Thermophysics 2019 (Cest). In: AIP Conference Proceedings 2116(1), 060007, July 2019 (2019). https://doi.org/10.1063/1.5114042
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Dushkin, R.V., Stepankov, V.Y. (2023). Principles of Solving the Symbol Grounding Problem in the Development of the General Artificial Cognitive Agents. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 544. Springer, Cham. https://doi.org/10.1007/978-3-031-16075-2_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-16075-2_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16074-5
Online ISBN: 978-3-031-16075-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)