Self-learning of autonomous intelligent robots in the process of search and explore activities

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.