This paper proposes an effective autonomous task allocation method that can achieve efficient cooperative work by divisional cooperation in multi-Agent contexts. Computer and network technology has enabled agents/robots to behave autonomously and to be used in a variety of applications such as cleaning and security patrolling. However, to cover large environments, cooperation and collaboration among several agents are mandatory for efficiency and for the required task quality. However, how agents cooperate is a challenging issue because actual environments are usually complicated and because their own (very uncommon) characteristics. Thus, we first define the continuous cooperative patrolling problem, in which agents split up and move around the environments with the required frequencies that are defined for every location. Then, we extend the previous cooperation method to prompt autonomous and effective division of labor by introducing the negotiation for task (re)allocations. We experimentally show that agents with our method enable effective division and fair allocation by identifying their own responsible locations in a bottom-up manner and that they could achieve considerably improved results compared with those of the previous method. We also investigated the structure of the resulting regime for cooperation and analyzed why our method could achieve the effective task allocation.