Co-evolution Learning in Organizational-Learning Classifier System
Takao Terano, Yasushi Ishikawa · 2002
This paper proposes an agent-based system with Organizational-Learning Oriented Classifier System (OCS), which is an extension of Learning Classi-fier System ~CS) into multiple agent environments. In OCS, each agent is equipped with a correspond-ing Michigan type LCS and acquires problem solv-ing knowledge based on the concepts of organizational learning in management and organizational sciences. In the proposed system, we further extend OCS to employ the following characteristics: (1) each agent solves multi-objective problems, there are some trade-offs about given problems; (2) the agents compose multi-classes, and in each class, they pursue different goals, which might cause conflicts among the classes of agents, and (3) the agents learn both individually and organizationally. We have applied the system to logical Marketing " domain in order to explain competi-tive and cooperative agent behaviors in developing and purchasing both economical nd ecological products.