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

V. Lokhin, S. Manko, M. Romanov, I. Gartseev, M. V. Kadochnikov
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引用次数: 1

Abstract

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
基于遗传算法的智能机器人系统自动教育与自组织
作为智能系统的主要特征之一,在先验的未定义和可变条件下有效运行的可能性,大多是由它们的自我教育和自组织能力预先确定的。因此,将获得的经验泛化、自动形成和扩充知识的问题在学术上和应用上都很有趣。对现有方法的阐述和解决这些问题的新方法的发展,为创造各种类型和目的的智能自我教育系统提供了坚实的基础,这些系统在使自己的行为适应环境的行动、预测情况的变化、揭示现有模式等方面具有广泛的能力。对于智能控制系统的自动知识库合成问题,最有趣和最有前途的方法之一与所谓的遗传算法的使用有关
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