## 3 Methodology
### Dynamic Task Allocation
Multiple static targets scenario: The mission is to detect, identify and attack multiple targets. For example, a missile defense system includes a radar system, command and control center, communication system and missile launchers [39]. A fleet of agents are taking off from the same site must be able to use the acquired knowledge to evaluate and group to smaller squads to execute their mission independently. 3. Multiple dynamic targets scenario: Number of targets may increase or decrease during the operation before all agents reach their destinations. * Decrease target scenario: Targets may be canceled or disappear during operation. For example, a fleet of agents was launched to survey and attack an anti-missile defense system which includes the ground radars, interceptors, command and control centers. Agents take off from the same base and are grouped into four subgroups to attack individual targets. During the movement, the enemy command center may move to a new location away from the mission range. The number of targets decreased to three. It is also prioritized to destroy the ground radar system to disable the enemy's detection capability. * Increase target scenario: New targets may also appear during the operation. For example, the initial mission is to survey the enemy command center. The enemy adds two interceptor launchers within the operation area before agents reach the target. Thus, the target number increases from one to three (command center and two interceptor launchers). ### Experiments

We implemented the autonomous agents system with Python tool kit on a PC with Intel (R) Core(TM) i7-8550 running at 1.8 and 1.99 GHz and used simulation to present the results of task assignment of agents with our C-CBAA method and Committee-Based Consensus Dynamic Trajectory Planning algorithm to show their 2D path with different scenarios. Four cases were simulated to demonstrate how agents can coordinate task assignment and path planning autonomously. Section 4.2.1 shows the flexibility and efficiency of our trajectory planning compression by passing through a complex environment. Section 4.2.2 focuses on the case of static target scenario. Section 4.2.3 shows the result of assignment change during the movement by either increasing or decreasing the number of targets. Section 4.2.4 compares the differences between the proposed C-CBAA method with the traditional CBAA algorithm. The parameters used in our simulation are shown in Table 1.

The followings are the simulation results of multi-agents in different scenarios. The dash lines are the flight trajectories of agents. Agents are colored dots and different color represents different subgroup. The triangle symbols are the targets. Greyed areas and black dots represent the obstacles. #### 4.2.1 Passing through Complex Environment

To evaluate the capability and flexibility of the configuration formation change in terms of compactness, we set up a narrow passage for the teams to pass through. In the simulation, agents are expected to change their formation by switching their _moving_ states to _waiting_ states or vice versa in order to orderly pass through the narrow passage. The result is shown in Figure 3 where a team of 8 agents start from the left and the target is set on the right. We use the color for agents to identify the states while passing through the passage, Figure 3a is evident that both sides of the agents predict the inter-collision may occur before entering and therefore they wait for the other teammates to pass first. In Figure 3b where the green agents represent the ones in a _moving_ state and the black ones are for the _waiting_ state. More complex obstacles are designed to evaluate the comprehensive functionality of agents in changing formation and finding a path to get around the obstacles. Due to the range limitation of sensors, agents cannot predict the obstacles that may block their moving direction far away in advance.