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Learning Through Direct and Indirect Communications for Distributed Control in Networked Systems

We consider collaborative decision making and control in multi-agent systems. The emphasis is to derive as simple as possible distributed algorithms that work provably very well, while having minimal knowledge of the system and its parameters; thus the need for distributed learning. We describe our results on three problems. First, we consider a behavior learning algorithm for a class of behavior functions and study its effects on the emergence of agent collaboration. Second, we consider multi-agent systems, with each agent picking actions from a finite set and receiving a payoff depending on the actions of all agents. The exact form of the payoffs is unknown and only their values can be measured by the agents. We develop a distributed algorithm that leads to welfare optimizing agent actions utilizing the impact on payoffs from agents’ actions, and if needed simple, bit-valued information exchanges between the agents. We consider also the continuous time and continuous state space version of the problem based on ideas from extremum seeking control. Third, we consider consensus problems with adversaries and develop distributed converging algorithms, with much weaker connectivity requirements than prior work, through the use of a dynamic trust mechanism that helps to detect and isolate adversaries. Our results show how indirect communications (agents signaling via the impact of their actions) and direct communications (messages sent between the agents) can complement each other and lead to simple distributed control algorithms with remarkably good performance. Several applications are briefly discussed. We close by describing current and future research directions.

Type of Seminar:
IfA Seminar
Prof. John Baras
Institute for Systems Research, University of Maryland College Park, USA
Feb 05, 2016   11.00 am

Contact Person:

Prof. John Lygeros
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Biographical Sketch:
John S. Baras, Lockheed Martin Chair in Systems Engineering Diploma in Electrical and Mechanical Engineering from the National Technical University of Athens, Greece, 1970; M.S., Ph.D. in Applied Mathematics from Harvard University 1971, 1973. Since 1973, faculty member in the Electrical and Computer Engineering Department, and in the Applied Mathematics, Statistics and Scientific Computation Program, at the University of Maryland College Park. Since 2000, faculty member in the Fischell Department of Bioengineering. Since 2014, faculty member in the Mechanical Engineering Department. Founding Director of the Institute for Systems Research (ISR), 1985 to 1991. Since 1991, Founding Director of the Maryland Center for Hybrid Networks (HYNET). Since 2013, Guest Professor at the Royal Institute of Technology (KTH), Sweden. IEEE Life Fellow, SIAM Fellow, AAAS Fellow, NAI Fellow, and a Foreign Member of the Royal Swedish Academy of Engineering Sciences (IVA). Received the 1980 George Axelby Prize from the IEEE Control Systems Society, the 2006 Leonard Abraham Prize from the IEEE Communications Society, the 2014 Tage Erlander Guest Professorship from the Swedish Research Council, and a three year (2014-2017) Senior Hans Fischer Fellowship from the Institute for Advanced Study of the Technical University of Munich, Germany. Professor Baras' research interests include automatic control communication and computing systems and networks, and model-based systems engineering.