SELF-LEARNING OF AUTONOMOUS INTELLIGENT ROBOTS IN THE PROCESS OF SEARCH AND EXPLORE ACTIVITIES; [САМООБУЧЕНИЕ АВТОНОМНЫХ ИНТЕЛЛЕКТУАЛЬНЫХ РОБОТОВ В ПРОЦЕССЕ ПОИСКОВО-ИССЛЕДОВАТЕЛЬСКОЙ ДЕЯТЕЛЬНОСТИ∗]

One of the effective approaches to organizing the goal-seeking behavior of autonomous integral robots in the process of search and explore activities in an a priori undescribed conditions of a problematic environment is considered. It is proposed to use the procedures of visual-effective thinking based on the formalization of the reflex behavior of highly organized living systems as the basis for the goal-seeking behavior of robots. A self-learning algorithm has been developed for the conditions with a high level of uncertainty which allows automatically generating conditional programs of expedient behavior that provide autonomous integral robots with the ability to achieve a given behavioral goal in the process of search and explore activities. The boundary estimates of the functional complexity of the proposed self-learning algorithm under uncertainty are found showing the possibility of its implementation on the onboard computer of autonomous integral robots which have, as a rule, limited computing resources. A modeling of self-learning process for an autonomous integral robot in an a priori undescribed and problematic environment was carried out which confirmed the effectiveness of the proposed approach for organizing the planning of goal-seeking behavior in an a priori undescribed and problematic environments. © 2023 Federal Research Center "Computer Science and Control" of Russian Academy of Sciences. All rights reserved.

Авторы
Melekhin V.B. , Khachumov V.M. , Khachumov M.V.
Издательство
Федеральный исследовательский центр "Информатика и управление" РАН
Номер выпуска
2
Язык
Русский
Страницы
78-83
Статус
Опубликовано
Том
17
Год
2023
Организации
  • 1 Department of Software for Computers and Automated Systems, Dagestan State Technical University, 70A Imam Shamil Ave., Makhachkala, 367015, Russian Federation
  • 2 Federal Research Center “Computer Science and Control”, the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow, 119333, Russian Federation
  • 3 Department of Information Technology, RUDN University, 6 Miklukho-Maklaya Str., Moscow, 117198, Russian Federation
  • 4 Intelligent Control Laboratory, Ailamazyan Program Systems Institute, the Russian Academy of Sciences, 4A Petra Pervogo Atr., Yaroslavl Region, Veskovo, 152024, Russian Federation
Ключевые слова
autonomous integral robot; conditional signals; problematic environment; self-learning algorithm; uncertainty conditions
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