A graph neural network learns to approximate altruistic robot transfers across heterogeneous teams using Hamilton's rule, achieving near-optimal allocation in simulated firefighting scenarios.
Submodular function maximization,
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Introduces DGBA for submodular task allocation in MAS under q-independence constraints, claiming polynomial-time feasibility with approximation guarantees and better performance than benchmarks in simulations.
citing papers explorer
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Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems
A graph neural network learns to approximate altruistic robot transfers across heterogeneous teams using Hamilton's rule, achieving near-optimal allocation in simulated firefighting scenarios.
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Distributed Task Allocation for Multi-Agent Systems: A Submodular Optimization Approach
Introduces DGBA for submodular task allocation in MAS under q-independence constraints, claiming polynomial-time feasibility with approximation guarantees and better performance than benchmarks in simulations.