Using limited memory to store the most recent action in XCS learning classifier systems in maze problems

Nowadays, learning classifier systems have received attention in various applications in robotics, such as sensory robots, humanoid robots, intelligent rescue and rescue systems, and control of physical robots in discrete and continuous environments. Usually, the combination of an evolutionary algorithm or intuitive methods with a learning process is used to search the space of existing rules in assigning the appropriate action of a category. The important challenge to increase the speed and accuracy in reaching the goal in the maze problems is to use and choose the action that the stimulus is placed on the right path instead of repeatedly hitting the surrounding obstacles. For this purpose, in this article, an intelligent learning classifier algorithm of accuracy-based learning classifier systems (XCS) based on limited memory is used, which according to the input and actions applied to the environment and the reaction of the stimulus, the rules It is optimally identified and added as a new classifier set to the accuracy-based learning classifier systems (XCS) algorithm in the next steps. Among the achievements of this method, it can be based on reducing the number of necessary steps and increasing the speed of reaching the stimulus to the target compared to the accuracy-based learning classifier systems (XCS) algorithm.