Multi-robot system learning based on evolutionary classification
Moscow State University of Information Technologies, Radio Engineering and Electronics (MIREA), Russia
a Corresponding author: email@example.com
This paper presents a novel machine learning method for agents of a multi-robot system. The learning process is based on knowledge discovery through continual analysis of robot sensory information. We demonstrate that classification trees and evolutionary forests may be a basis for creation of autonomous robots capable both of learning and knowledge exchange with other agents in multi-robot system. The results of experimental studies confirm the effectiveness of the proposed approach.
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.