MetaOrch is a deep learning orchestrator that selects optimal agents in multi-agent systems via fuzzy-scored supervision labels, reporting 86.3% accuracy in simulations over random and round-robin baselines.
John Wiley & Sons (2007)
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Neural Orchestration for Multi-Agent Systems: A Deep Learning Framework for Optimal Agent Selection in Multi-Domain Task Environments
MetaOrch is a deep learning orchestrator that selects optimal agents in multi-agent systems via fuzzy-scored supervision labels, reporting 86.3% accuracy in simulations over random and round-robin baselines.