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.