A Study of Autonomous Agent Navigation Algorithms in the ANIMAT Environment

This paper is dealt with the examination of possibilities of the algorithm of autonomous agent navigation. The ANIMAT problem was proposed to study the possibilities of ZCS classifier system learning. A ZCS classifier system is purely reactive. For this system it is essential to have a possibility to identify the global state of the environment by the input message. This shortens the application area of the system and its ability to solve complex tasks. The present paper proposes to build an alternative learning algorithm that would be able to cope with the above mentioned problems. The effectiveness of the suggested algorithm is tested via practical experiments. In the experiments performed the algorithm has demonstrated its essential superiority over a classifier system with temporary memory.