Ontoepisociophylogenetic development of artificial general intelligence systems based on multi-agent neurocognitive architectures

The purpose of the study is to study the possibilities of multi-generational optimization of control systems based on multi-agent neurocognitive architectures to create general artificial intelligence agents capable of independently solving a universal range of tasks in a real environment. The main principles for achieving the adaptive stability of general artificial intelligence agents based on multi-agent neurocognitive architectures to the operating conditions based on ontophylogenetic learning in the process of synthesis of problem solving over dynamic decision trees are developed. The basic principles for constructing algorithms for multi-generational optimization of the structural and functional organization of general artificial intelligence agents based on multi-agent neurocognitive architectures, taking into account genetic, ontological and social factors, have been developed.