This paper deals with a task-assignment architecture for cooperative transport by multiple mobile robots in an unknown static environment. The architecture should satisfy three features: deal with a variety of tasks in time and space, deal with a large number of tasks compared with the number of robots, and decide the behavior in real time. The authors propose the following approach: we consider the unit of task (task instance) as the job that should be done in a short time by one robot. Based on the environmental information, task instances are dynamically generated using task templates. The priority of task instances is evaluated dynamically based on the number of robots and the configuration in the workspace. In addition, we avoid generating too many task instances by suppressing object motion. The main part of the architecture consists of two real-time planners: a priority-based task-assignment planner solved by using the linear programming method, and motion planners based on short-time estimation. The effectiveness of the proposed architecture is verified by a cooperative transport simulation in an unknown environment.