Accession Number : AD1016962


Title :   Distributed Learning, Extremum Seeking, and Model-Free Optimization for the Resilient Coordination of Multi-Agent Adversarial Groups


Descriptive Note : Technical Report,02 Jul 2012,30 Jun 2016


Corporate Author : CALIFORNIA UNIV SAN DIEGO LA JOLLA LA JOLLA United States


Personal Author(s) : Martinez,Sonia ; Krstic,Miroslav


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1016962.pdf


Report Date : 07 Sep 2016


Pagination or Media Count : 16


Abstract : This proposal focuses on the analysis and design of coordination algorithms for multiple agents deployed in adversarial environments. The multi-agent systems can represent miscellaneous autonomous and semi-autonomous vehicles that are remotely controlled by operators. These groups can be subject to attacks from other external agents leading to complex networked adversarial settings. The proposal presents work in two main areas: 1) the use of a class of receding-horizon type of algorithms to overcome the effect of a type of uncoordinated attackers on a multi-vehicle-operator group, and 2) the use of extremum seeking (ES) techniques to learn Nash equilibria in finitely- and infinitely-many player noncooperative games and to solve high-dimensional optimization problems. Extensions and applications of these techniques were developed during the realization of the project.


Descriptors :   algorithms , multiagent systems , control systems , autonomous vehicles , optimization , learning , nonlinear systems


Distribution Statement : APPROVED FOR PUBLIC RELEASE