Extension properties and boundary estimates for a fractional heat operator

Instrumental Means for Managing the Rational Behavior of Self-Organizing Autonomous Intelligent Agents

Mekhatronika Avtomatizatsiya Upravlenie ◽

10.17587/mau.22.171-180 ◽

2021 ◽

Vol 22 (4) ◽

pp. 171-180

Author(s):

V. B. Melekhin ◽

M. V. Khachumov

Keyword(s):

Intelligent Agents ◽

Environmental Changes ◽

A Priori ◽

Operating Conditions ◽

Basic Principles ◽

Purposeful Behavior ◽

Problem Solvers ◽

Boundary Estimates ◽

Self Learning ◽

Self Organizing

We formulate the basic principles of constructing a sign-signal control for the expedient behavior of autonomous intelligent agents in a priori undescribed conditions of a problematic environment. We clarify the concept of a self-organizing autonomous intelligent agent as a system capable of automatic goal-setting when a certain type of conditional and unconditional signal — signs appears in a problem environment. The procedures for planning the expedient behavior of autonomous intelligent agents have been developed, that imitate trial actions under uncertainty in the process of studying the regularities of transforming situations in a problem environment, which allows avoiding environmental changes in the process of self-learning that are not related to the achievement of a given goal. Boundary estimates of the proposed procedures complexity for planning expedient behavior are determined, confirming the possibility of their effective implementation on the on-board computer of the automatic control system for the expedient activity of autonomous intelligent agents. We carry out an imitation on a personal computer of the proposed procedures for planning purposeful behavior, confirming the effectiveness of their use to build intelligent problem solvers for autonomous intelligent agents in order to endow them with the ability to adapt to a priori undescribed operating conditions. The main types of connections between various conditional and unconditional signal — signs of a problem environment are structured, which allows autonomous intelligent agents to adapt to complex a priori undescribed and unstable conditions of functioning.

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