A possible approach to the creation of self-learning control systems for autonomous robots

Unmanned systems often function in new, non-predetermined environment. This demands flexible and simultaneously stable function of these systems. This can be implemented by their adaptation and, as a final goal, self-learning or self-organization in control systems. Autonomous functioning in extreme environment requires self-learning drastically. Such environment can be hostile towards unmanned systems, it may contain sophisticated communication conditions. All these are deteriorated by poor on-board control algorithms. In some of such cases unmanned systems may become inefficient or, even more, useless in some cases. This brings to front necessity to create adaptive self-learning control systems for robots. These should be capable to generate efficient, or even optimal decisions in extreme environment. This paper presents on of possible approaches to create self-learning control systems based on so called decision along analogues.