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This article is cited in 1 scientific paper (total in 1 paper)
Information Technologies and Telecommunications
Ontophylogenetic algorithms for the synthesis of intellectual phenotypes
of software agents for use in tasks of multigenerational optimization
of control neurocognitive architectures
A. Z. Apsheva, B. A. Atalikovb, S. A. Kankulovb, D. A. Malyshevb, Z. A. Sundukovb, A. Z. Enesb a Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences,
360010, Russia, Nalchik, 2 Balkarov streetk
b Institute of Computer Science and Problems of Regional Management –
branch of Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences,
360000, Russia, Nalchik, 37-a I. Armand street
Abstract:
The purpose of the study is to develop methods and algorithms for the ontophylogenetic
synthesis of artificial intelligence software agents based on multi-agent neurocognitive architectures that
allow combining the situationality and explanatory power of reinforcement learning and the adaptive
efficiency and stability of genetic algorithms. An algorithm for synthesizing the phenotypes of control
systems of intelligent agents based on the data of their genotypes has been developed. A software package
for simulating the processes of ontophylogenetic synthesis of multi-agent neurocognitive architectures has
been also developed. Experiments were carried out to create phenotypes of intelligent agents based on the
developed genotypes of control multi-agent neurocognitive architectures.
Keywords:
artificial intelligence, multi-agent systems, genetic algorithms, ontophylogenetic learning.
Received: 01.12.2022 Revised: 08.12.2022 Accepted: 15.12.2022
Citation:
A. Z. Apshev, B. A. Atalikov, S. A. Kankulov, D. A. Malyshev, Z. A. Sundukov, A. Z. Enes, “Ontophylogenetic algorithms for the synthesis of intellectual phenotypes
of software agents for use in tasks of multigenerational optimization
of control neurocognitive architectures”, News of the Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences, 2022, no. 6, 76–91
Linking options:
https://www.mathnet.ru/eng/izkab515 https://www.mathnet.ru/eng/izkab/y2022/i6/p76
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Abstract page: | 38 | Full-text PDF : | 4 | References: | 8 |
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