Co-Evolution Learning in Organizational-Learning Classifier System

Takao Terano and Yasushi Ishikawa

This paper proposes an agent-based system with Organizational-Learning Oriented Classifier System (OCS), which is an extension of Learning Classifier System (CS) into multiple agent environments. In OCS, each agent is equipped with a corresponding Michigan type LCS and acquires problem solving 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 tradeoffs 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 competitive and cooperative agent behaviors in developing and purchasing both economical and ecological products.

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