Metamodel-Driven Modeling of UAF-Based Cooperative Drone Combat Systems with AutoCL-MAPPO

Traditional MBSE faces challenges in verifying the dynamic performance of autonomous systems due to a semantic gap between static architecture and executable algorithms. To address this limitation, this study proposes a system-of-systems (SoS) modeling methodology that connects the Unified Architecture Framework (UAF) with multi-agent reinforcement learning. A Drivers–Challenges–Opportunities–Goals (DCOG) framework maps strategic intent to operational capabilities, which are formalized in the UAF. Driven by the UAF Domain Metamodel (DMM), a pipeline transforms behavioral and resource specifications into a Markov Decision Process (MDP). An Automatic Curriculum Learning Multi-Agent Proximal Policy Optimization (AutoCL-MAPPO) algorithm then resolves the MDP. The autonomous fleet achieves a 73% mission success rate, outperforming the standard MAPPO baseline (65%). These performance metrics are averaged over 1000 independent test episodes to ensure statistical significance, with baseline algorithms evaluated under identical environmental conditions. Using Systems Modeling Language (SysML) as a verification carrier, activity simulations confirm that the generated decision sequences conform to UAF structural logic. Metric constraint deviation analysis provides empirical feedback for iterative design refinement. This methodology establishes a verifiable digital thread, closing the loop between architecture modeling and learned behavior for autonomous SoS and Human–AI Teaming.