🤖 AI Summary
This study addresses the challenge of achieving parameter consensus and evaluating individual capabilities under heterogeneous trust levels in multi-agent systems. To this end, it proposes a weighted centroid iterative algorithm based on Bregman divergence. By fusing parameter estimates from heterogeneous agents, this approach drives the consensus process to naturally yield a quantitative assessment mechanism for individual agent capabilities. Theoretically, the algorithm is proven to converge to a unique consensus estimate while generating computable collective weights. Practically, it integrates decentralized agents into a collective super-agent equipped with built-in capability evaluation, thereby establishing a novel paradigm for trustworthy multi-agent collaboration.
📝 Abstract
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.