Learning by Teaching versus Learning by Doing: Knowledge Exchange in Organic Agent Systems

摘要

"Learning by doing" and "learning by teaching" are two important concepts for human education. In this article, we demonstrate that these learning concepts can also be realized by intelligent, so-called organic computing systems. These organic agents either improve their skills by themselves, eventually assisted by a teacher, or they teach each other by exchanging learned rules. We show that "learning by teaching" may reduce the query costs for teachers and allow for a proactive behavior of organic agents: Before certain situations emerge in their environment, they are already enabled to deal with that situations. We also show that "learning by teaching" may be problematic in cases where different agents are expected to have - at least partially - different skills. Then, incautious knowledge exchange may yield a performance degradation. There are many possible application fields for these organic systems, e.g., distributed intrusion detection, robotics, or sensor networks.