An agent-based framework for bio-inspired, value-sensitive decision-making

Abstract We propose a generalizable framework that uses tools of nonlinear dynamics to rigorously connect model-based investigation of the mechanisms of animal group decision-making dynamics to systematic, bio-inspired design of coordinated control of multi-agent systems. We focus on the design of networked multi-agent system dynamics that inherit the remarkable features of value-sensitive decision-making observed in house-hunting honeybees. These features include robustness and adaptability in decision-making, all of which are critical for performance in complex, changing environments.

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