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Department of Engineering and Naval Architecture (DITEN), University of Genoa, 16145 Genoa, Italy
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UAV swarms can now adapt to changing conditions and replan trajectories in real-time by inferring expert-like behaviors from a learned probabilistic world model, avoiding computationally expensive re-optimization.
Active Inference offers a scalable and cognitively grounded alternative to reinforcement learning for UAV swarm control, showing faster convergence and improved stability in dynamic environments.
