论文标题
在多基因系统（扩展版本）中学习信任对有向图的信任（扩展版）
Learning Trust Over Directed Graphs in Multiagent Systems (extended version)
论文作者
论文摘要
当未知子集由恶意参与者组成时，我们解决了多种网络中其他代理商合法性的问题。我们特异性地得出了定向图的情况以及随机侧信息或信任的观察结果。我们将其称为``学习信任''，因为代理必须确定网络中的哪些邻居是可靠的，并且我们得出了实现这一目标的协议。我们还提供了分析结果表明，在此协议下i）代理几乎可以肯定地学习所有其他代理的合法性，而ii）代理人的意见融合了网络中所有其他代理的真正合法性。最后，我们提供的数值研究表明，我们的收敛结果在各种网络拓扑以及网络中恶意药物数量的变化中成立。
We address the problem of learning the legitimacy of other agents in a multiagent network when an unknown subset is comprised of malicious actors. We specifically derive results for the case of directed graphs and where stochastic side information, or observations of trust, is available. We refer to this as ``learning trust'' since agents must identify which neighbors in the network are reliable, and we derive a protocol to achieve this. We also provide analytical results showing that under this protocol i) agents can learn the legitimacy of all other agents almost surely, and that ii) the opinions of the agents converge in mean to the true legitimacy of all other agents in the network. Lastly, we provide numerical studies showing that our convergence results hold in practice for various network topologies and variations in the number of malicious agents in the network.
