level: research
this paper introduces a three level learning architecture for autonomous uav swarms in search and rescue. the design mirrors biological hierarchies: reflexes, skills, and reasoning. each level uses a different learning method. the lowest level uses hebbian neuroplasticity for individual drone adaptation. the middle level applies multi-agent reinforcement learning with graph neural networks and behavior trees for team coordination. the top level employs model-agnostic meta-learning with bdi reasoning and a digital twin for strategic decisions.
the architecture is defined by twenty-two contracts across six components: bdi, behavior trees, graph neural networks, multi-agent reinforcement learning, neuroplasticity, and meta-learning. these contracts provide six classes of formal guarantees, including safety and budget correctness. the separation of learning mechanisms allows each layer to handle its specific task without interference. this structured approach aims to make swarm behavior more predictable and reliable in dynamic environments.
by combining these distinct learning paradigms, the system can adapt from low-level motor control to high-level mission planning. the digital twin enables simulation-based training and strategy refinement before real-world deployment. the integration of formal contracts helps verify that the swarm meets critical requirements. this work moves beyond single-algorithm solutions, offering a blueprint for complex multi-agent systems that need both autonomy and safety.
why it matters: it provides a structured way to build reliable autonomous drone swarms for critical tasks like search and rescue, with formal safety guarantees.