Design of a Task-Driven Multi-Agent Alliance Optimization Mechanism in Land Warfare Context

In the context of land warfare, collaborative operations of multi-agent systems have become an important means to enhance combat effectiveness. This paper proposes a multi-agent alliance optimization mechanism oriented towards task updates, aiming to improve the collaborative capabilities of multi-agents in complex battlefield environments by dynamically adjusting the alliance structure and employing reinforcement learning methods. Firstly, an agent alliance construction mechanism is designed based on task requirements and changes in the operational environment to ensure the effective allocation and execution of task objectives. Secondly, a task update mechanism is introduced to dynamically adjust the cooperation relationships among alliance members to respond to sudden changes on the battlefield. Four types of agents (reconnaissance, decision-making, strike, and support) are assigned distinct roles and functions, with reinforcement learning employed to continuously optimize their decision-making and execution processes, enabling collaborative optimization among agents. Experimental results show that this mechanism significantly improves the execution efficiency and task completion of multi-agent alliances in complex tasks and demonstrates strong adaptability and robustness in dynamically changing operational environments.