In development of Expert Systems (ES), it important to acquire knowledge from multiple human experts. Cooperative Distributed ES (CDES) is a framework which can unify multiple expertise to develop a large scaled ES. In CDES, ESs which are in the process of building through acquiring knowledge from human experts correspond to agents, and task structure is learned by cooperation among agents based on extended Contract Net. At first, the fundamental structure of CDES is provided. An experiment is made to evaluate algorithms of a Bid analyzer on a testbed of CDES. Then, self-reorganizational schemes are provided by changing the agent’s scope of domain knowledge, that is granularity, by themselves, from two viewpoints -- one of decomposing into flue grain, and the other of composing into coarse grain. Two experiments are made to learn the subtask structure to change the weight of cooperation among agents dynamically. Finally, we discuss the scheme which incorporates decomposition and composition of agents.