<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">LeBlanc, Heath</style></author><author><style face="normal" font="default" size="100%">Koutsoukos, Xenofon</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Consensus in Networked Multi-Agent Systems with Adversaries</style></title><secondary-title><style face="normal" font="default" size="100%">Hybrid Systems: Computation and Control</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">04/2011</style></date></pub-dates></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://archive.isis.vanderbilt.edu/sites/default/files/hscc21c-leblanc.pdf</style></url></related-urls></urls><abstract><style face="normal" font="default" size="100%">In the past decade, numerous consensus protocols for networked
multi-agent systems have been proposed. Although some forms of
robustness of these algorithms have been studied, reaching consensus
securely in networked multi-agent systems, in spite of intrusions
caused by malicious agents, or adversaries, has been largely
underexplored. In this work, we consider a general model for adversaries
in Euclidean space and introduce a consensus problem for
networked multi-agent systems similar to the Byzantine consensus
problem in distributed computing. We present the Adversarially
Robust Consensus Protocol (ARC-P), which combines ideas from
consensus algorithms that are resilient to Byzantine faults and from
linear consensus protocols used for control and coordination of dynamic
agents. We show that ARC-P solves the consensus problem
in complete networks whenever there are more cooperative agents
than adversaries. Finally, we illustrate the resilience of ARC-P to
adversaries through simulations and compare ARC-P with a linear
consensus protocol for networked multi-agent systems.</style></abstract></record></records></xml>