Automatic education and self organization of intelligent robotic systems based on genetic algorithms

The possibility of efficient functioning in a priori undefined and changeable conditions, being one of the major features of intelligent systems, is mostly predefined by their abilities in self-education and self-organization. Therefore the problems of generalizing acquired experience, automatically forming and augmenting knowledge are both interesting academically and significant for applications. The elaboration of the existing approaches and the development of new ways of solving these problems provides a substantial basis for the creation of intelligent self-educating systems of various types and purposes, possessing a wide set of abilities in adapting one's behavior to the environment's actions, forecasting the changes of situation, exposing the existing patterns, etc. One of the most interesting and promising approaches to the problem of automatic knowledge base synthesis for intelligent control systems is connected with the use of so-called genetic algorithms