Open AccessSituational analysis model in an intelligent system based on multi-agent neurocognitive architectures
Author(s) -
Zalimkhan Nagoev,
Inna Pshenokova,
Olga Nagoeva,
S.A. Kankulov
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2131/2/022103
Subject(s) - neurocognitive , computer science , artificial intelligence , situational ethics , intelligent agent , agent architecture , process (computing) , human–computer interaction , cognition , machine learning , intelligent decision support system , multi agent system , psychology , social psychology , neuroscience , operating system
An approach to the development of intelligent decision-making and control systems based on the hypothesis of the organization of neural activity of the brain in the process of performing cognitive functions is proposed. This approach, based on intelligent software agents with a developed cognitive architecture, is able to provide the process of extracting knowledge from an unstructured data flow, generalizing the knowledge and learning gained, to implement effective methods of synthesizing behavior aimed at solving various problems. A multi-agent model of situational analysis based on self-organization of distributed recursive neurocognitive architectures is presented. In particular, the basic principles of situational analysis based on multi-agent neurocognitive architectures are formulated and an algorithm for the preventive synthesis of the behavior of an intelligent agent aimed at avoiding negative situations for itself is developed. The performed computational experiment showed that on the basis of training the neurocognitive architecture by forming new agents-neurons and connections between them, a complex logical function of behavior control (in particular, situational analysis) develops (forms). The results of this study can be used to create intelligent decision-making and control systems for autonomous robots and robotic systems for various purposes.