Cooperative Control and Potential Games

By Jason R. Marden, Gürdal Arslan, Jeff S. Shamma. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2009

539 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/f06d672b657ad8448039cbe26918ae365170fb4c

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Citations in the corpus

Modeling, Analysis and Control of Networked Evolutionary Games (2015, background): “For instance, the FP (fictitious play), which needs all the historic knowledge to update its strategy [36]; the SAP (spatial adaptive player) which has time-varying topology [35].”
Distributed Seeking of Nash Equilibria With Applications to Mobile Sensor Networks (2012, methodology): “Namely, by taking the global coverage control objective function (as defined in [31]) as a potential function, it is possible to assign, so called, Wonderful Life individual cost functions [3], [32], [33] to each agent.”
Opinion dynamics in social networks with stubborn agents: Equilibrium and convergence rate (2014, background): “The references [34], [35] demonstrate how cooperative control problems, e.”
A systems and control perspective of CPS security (2019, background): “For instance, the evolution of network control systems has been modeled as cooperative games [126] and the resilience of these cooperative games to the actions of adversarial agents or communication failures have been investigated.”
Distributed Nash Equilibrium Seeking by a Consensus Based Approach (2016, background): “, [5], [7], [8], [9]), this paper addresses Nash equilibrium seeking under local communication network, i.”
Game-Theoretic Methods for the Smart Grid: An Overview of Microgrid Systems, Demand-Side Management, and Smart Grid Communications (2012, unknown): “To this end, one of the recently emerging areas is that of designing incentives to enforce cooperation in a n oncooperative setting, such as in [3] and [4].”
Opportunistic Spectrum Access in Cognitive Radio Networks: Global Optimization Using Local Interaction Games (2012, background): “It is shown that in potential games, spatial adaptive play (SAP) which is a learning algorithm [17], [44], converges to a pure NE that maximizes the potential function with arbitrarily higher probability.”