Researchers have developed a new methodology for mission coordination in complex Systems of Systems (SoS) by integrating high-fidelity digital mission models with Reinforcement Learning (RL). This approach formulates task allocation as a Markov Decision Process, training an RL coordinator using Proximal Policy Optimization to adapt to dynamic mission environments. An aerial firefighting mission simulation demonstrated that coordinators trained on multiple scenarios concurrently outperformed those trained on single scenarios, highlighting the importance of diverse training data for robust performance. AI
IMPACT This research could lead to more robust and adaptive mission coordination in complex systems, potentially improving efficiency in areas like disaster response.
RANK_REASON The cluster contains an academic paper detailing a new methodology and simulation results. [lever_c_demoted from research: ic=1 ai=1.0]
- aerial firefighting
- arXiv
- digital engineering
- Ibrahim Cetinkaya
- Markov decision process
- Mission Engineering Competencies
- Proximal Policy Optimization
- reinforcement learning
- Systems of systems analysis of national infrastructure
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