AI 中文总结
本文提出一种基于Bregman散度和加权重心的迭代共识算法，使智能体在参数估计上达成唯一共识，并形成对个体能力的集体评估。
AI 中文摘要
考虑一个由智能体组成的社区，这些智能体寻求在一组参数上达成共识。智能体同意使用相同的Bregman型散度来量化其各自对参数估计之间的分歧，但对彼此能力的信任程度各不相同。每个智能体都乐于通过移动到所有个体估计的加权重心来修正其估计，其中对更受信任的智能体赋予更高的权重。我们证明，这种修正自然导致一种迭代算法，该算法收敛到参数的唯一共识估计。此外，由于共识估计本身就是一个具有可计算权重的重心，该群体涌现为一个集体超级智能体，对每个个体智能体的能力形成了良好的意见。
英文摘要
Consider a community of agents who are seeking consensus on a set of parameters. The agents agree to use the same Bregman-type divergence to quantify disagreement between their individual estimates of the parameters but have varying confidence in each other's abilities. Each agent is happy to revise their estimate by moving to the weighted barycenter of all individual estimates with higher weights applied to more trusted agents. We show that such revisions naturally lead to an iterative algorithm which converges to a unique consensus estimate of the parameters. Furthermore, since the consensus estimate is itself a barycenter with computable weights, the group emerges as a collective super-agent with a well-formed opinion regarding the ability of each individual agent.
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