Geodesic
Simulation model of a neurocognitive control system
News of the Kabardin-Balkar scientific center of RAS, no. 6 (2023), pp. 226-234.

Voir la notice de l'article provenant de la source Math-Net.Ru

. A circuit of the control system of an intelligent agent has been developed, which ensures the formation of semantic ontologies of the “agent – environment” system, combining on the basis of interneuron associations functional representations of the current observed states of the agent and statements of counterparties in the communicative environment. Based on the ontoneuromorphogenesis algorithm, axo-dendronal connections grow, which is aimed at reflecting the cause-and-effect relationships between events that describe the context of the situation, events that describe the subject of the statement, and the event that describes the statement itself. According to the results of experiments of the simulation model it was concluded that the multi-agent algorithm for the autonomous synthesis of functional systems of semantic ontologies based on the growth and development of neurocognitive architectures can be applied to any subject area.
Keywords: control systems, multi-agent system, cognitive architectures
Mots-clés : intelligent agent 
@article{IZKAB_2023_6_a21,
     author = {Z. V. Nagoev and I. A. Pshenokova and O. V. Nagoeva and M. I. Anchekov and A. Z. Enes},
     title = {Simulation model of a neurocognitive control system},
     journal = {News of the Kabardin-Balkar scientific center of RAS},
     pages = {226--234},
     publisher = {mathdoc},
     number = {6},
     year = {2023},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/IZKAB_2023_6_a21/}
}
TY  - JOUR
AU  - Z. V. Nagoev
AU  - I. A. Pshenokova
AU  - O. V. Nagoeva
AU  - M. I. Anchekov
AU  - A. Z. Enes
TI  - Simulation model of a neurocognitive control system
JO  - News of the Kabardin-Balkar scientific center of RAS
PY  - 2023
SP  - 226
EP  - 234
IS  - 6
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/IZKAB_2023_6_a21/
LA  - ru
ID  - IZKAB_2023_6_a21
ER  - 
%0 Journal Article
%A Z. V. Nagoev
%A I. A. Pshenokova
%A O. V. Nagoeva
%A M. I. Anchekov
%A A. Z. Enes
%T Simulation model of a neurocognitive control system
%J News of the Kabardin-Balkar scientific center of RAS
%D 2023
%P 226-234
%N 6
%I mathdoc
%U http://geodesic.mathdoc.fr/item/IZKAB_2023_6_a21/
%G ru
%F IZKAB_2023_6_a21
Z. V. Nagoev; I. A. Pshenokova; O. V. Nagoeva; M. I. Anchekov; A. Z. Enes. Simulation model of a neurocognitive control system. News of the Kabardin-Balkar scientific center of RAS, no. 6 (2023), pp. 226-234. http://geodesic.mathdoc.fr/item/IZKAB_2023_6_a21/

[1] M. Lan, J. Xu, “Ontology feature extraction via vector learning algorithm and applied to similarity measuring and ontology mapping”, IAENG International Journal of Computer Science, 43(1) (2016), 10–19 | MR

[2] M. Somodevilla, D. Ayala, I. Pineda, “An overview on ontology learning tasks”, Comput. Sist., 22(1) (2018), 137–146

[3] M. Alobaidi, K. M. Malik, “Linked open data-based framework for automatic biomedical ontology generation”, BMC bioinformatics, 19(1) (2018), 3–19 | DOI

[4] M. N. Asim et al., “A survey of ontology learning techniques and applications”, Database, 2018 (2018), 101 | DOI

[5] Z. Ma, H. Cheng et al., “Automatic Construction of OWL Ontologies From Petri Nets”, International Journal on Semantic Web and Information Systems, 15(1) (2019), 21–51

[6] D. Maynard, A. Funk, W. Peters, “Using lexico-syntactic ontology design patterns for ontology creation and population”, Proc. of the Workshop on Ontology Patterns, v. 516, 2009, 39–52 pp.

[7] A. A. Dzyubanenko, A. V. Rabin, “Application of the method of metaontologies in the intellectual analysis of text resources considering the fuzziness and blurring of images of natural language units”, AIP Conference Proceedings, v. 2647, 2022 | DOI

[8] S. Elnagar, V. Yoon, M. A. Thomas, v. 05910, 2022 arXiv:2201

[9] N. Kumar, M. Kumar, M. Singh, “Automated ontology generation from a plain text using statistical and NLP techniques”, International Journal of System Assurance Engineering and Management, 7 (2016), 282–293

[10] D. E. Cahyani, I. Wasito, “Automatic ontology construction using text corpora and ontology design patterns (ODPs) in Alzheimer's disease”, J. Ilmu Komputer dan Informasi, 10(2) (2017), 59–66 | DOI

[11] R. Jabla, M. Khemaja, F. Buendia, S. Faiz, “Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments”, Applied Sciences, 11(22) (2021), 10770 | DOI

[12] Z. V. Nagoev, Intelligence, or thinking in living and artificial systems, Izdatel'stvo KBNTS RAN, Nalchik, 2013, 232 pp. (In Russian)

[13] Z. Nagoev, O. Nagoeva, I. Gurtueva, “Multi-agent neurocognitive models of semantics of spatial localization of events”, Cognitive Systems Research, 59 (2020), 91–102 | DOI

[14] K. Ch. Bzhikhatlov, S. A. Kankulov, D. A. Malyshev et al., “Interactive formation of spatial ontologies of an autonomous robot based on neurocognitive models of semantics”, Materials of the XVI All-Russian Scientific and Practical Conference and the XII Youth School-Seminar «Advanced Systems and Management Problems», Rostov-na-Dony, 2021, 147–154 (In Russian)

[15] Z. Nagoev, I. Pshenokova, O. Nagoeva, Z. Sundukov, “Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures”, Cognitive Systems Research, 66 (2021), 82–88 | DOI

[16] Z. V. Nagoev, “Multi-agent existential mappings and functions”, News of the Kabardino-Balkarian Scientific Center of RAS, 2013, no. 4 (54), 63–71 (In Russian)