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17 May 2013 Learning consensus in adversarial environments
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This work presents a game theory-based consensus problem for leaderless multi-agent systems in the presence of adversarial inputs that are introducing disturbance to the dynamics. Given the presence of enemy components and the possibility of malicious cyber attacks compromising the security of networked teams, a position agreement must be reached by the networked mobile team based on environmental changes. The problem is addressed under a distributed decision making framework that is robust to possible cyber attacks, which has an advantage over centralized decision making in the sense that a decision maker is not required to access information from all the other decision makers. The proposed framework derives three tuning laws for every agent; one associated with the cost, one associated with the controller, and one with the adversarial input.
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Kyriakos G. Vamvoudakis, Luis R. García Carrillo, and João P. Hespanha "Learning consensus in adversarial environments", Proc. SPIE 8741, Unmanned Systems Technology XV, 87410K (17 May 2013);

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