Artificial life (A-Life) is a new paradigm to realize a phenomena of life and to extract the hidden principles. One of the most attractive features in the A-Life approach is the emergence: simple elements interact each other based on lower level rules, and then the higher level complex phenomena could be emerged by the interaction. This paper proposes a new method for knowledge extraction and the integration based on an A-Life approach. The proposed system has two parts: the knowledge extraction network and the A-Life environment. The simple elements interact in the A-Life environment and the data is transferred to the knowledge extraction network. The knowledge is extracted in the form of rules in the rule layer and then they are fed back to the simple elements in the A-Life environment. We dealt with a path planning problem as an example of A-Life environment. In the simulation, we assumed a severe condition: the position of the goal was unknown to the robots. Since the robots did not know the goal in the initial condition, the trajectory by the first robot that reached the goal is very complicated. The trajectory data the robots had taken were inputted to the knowledge extraction network to extract rules. The trajectories become smooth step by step because of the extracted rules. We extracted various kinds of the rules using several different simple environments. By using the rules extracted from the simpler environments, the robot could reach the goal in a more complex environment.