Acquisition of a specialty in multi-agent learning: approach from learning classifier system
Hiroyuki Inoue, Katsunori Shimohara, Keiki Takadama, O. Katai · 2004
We focus on a multi-agent learning where plural agents acquire different specialties to achieve the system goal. This allows the system to solve the deadlock or malfunction problem where agents cannot realize the system goal due to a lack of coordination among the sub-goals pursued by the agents. To this end, this paper proposes an algorithm based on the learning classifier system that divides the sub-tasks that agents specialize in. Through experiments, it is shown that agents with the algorithm have greater potential compared to agents using the conventional learning classifier system when there are only a few agents in the system or the environment is too large for the conventional learning classifier system to learn effectively.