通过自适应调节意见动态，实现多智能体高效任务分工。
Adaptive bias for dissensus in nonlinear opinion dynamics with application to evolutionary division of labor games
- 结合非线性意见动态与演化博弈，设计自适应偏置控制策略。
- 理论证明可精准调控群体意见分布，实现两任务间的任意分配比例。
- 适用于多机器人协作、分布式任务分配等场景。
本文研究如何在非线性意见动态（NOD）中自适应调节偏置参数，以实现智能体在不同任务间的任意规模分组，从而最大化集体收益。此前工作已将NOD与多目标行为优化耦合，成功应用于多机器人自主任务分配的实地实验。受此启发，本文提出一种新任务分配模型，将NOD与演化博弈框架融合。理论上证明了在去中心化反馈机制下，通过自适应偏置可使群体意见状态收敛至期望的任务分配比例。进一步通过协同演化分工游戏的仿真验证了理论结果的有效性。
原文摘要 · Abstract (English)
This paper addresses the problem of adaptively controlling the bias parameter in nonlinear opinion dynamics (NOD) to allocate agents into groups of arbitrary sizes for the purpose of maximizing collective rewards. In previous work, an algorithm based on the coupling of NOD with an multi-objective behavior optimization was successfully deployed as part of a multi-robot system in an autonomous task allocation field experiment. Motivated by the field results, in this paper we propose and analyze a new task allocation model that synthesizes NOD with an evolutionary game framework. We prove sufficient conditions under which it is possible to control the opinion state in the group to a desired allocation of agents between two tasks through an adaptive bias using decentralized feedback. We then verify the theoretical results with a simulation study of a collaborative evolutionary division of labor game.
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