Geodesic
Ontophylogenetic algorithms for the synthesis of intellectual phenotypes
News of the Kabardin-Balkar scientific center of RAS, no. 6 (2022), pp. 76-91 Cet article a éte moissonné depuis la source Math-Net.Ru

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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. 
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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. News of the Kabardin-Balkar scientific center of RAS, no. 6 (2022), pp. 76-91. http://geodesic.mathdoc.fr/item/IZKAB_2022_6_a6/

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