KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Online Publication Date2017-07-12
Print Publication Date2017
Permanent link to this recordhttp://hdl.handle.net/10754/626970
MetadataShow full item record
AbstractIn distributed architecture control problems, there is a collection of interconnected decision-making components that seek to realize desirable collective behaviors through local interactions and by processing local information. Applications range from autonomous vehicles to energy to transportation. One approach to control of such distributed architectures is to view the components as players in a game. In this approach, two design considerations are the components’ incentives and the rules that dictate how components react to the decisions of other components. In game-theoretic language, the incentives are defined through utility functions, and the reaction rules are online learning dynamics. This chapter presents an overview of this approach, covering basic concepts in game theory, special game classes, measures of distributed efficiency, utility design, and online learning rules, all with the interpretation of using game theory as a prescriptive paradigm for distributed control design.
CitationMarden JR, Shamma JS (2017) Game-Theoretic Learning in Distributed Control. Handbook of Dynamic Game Theory: 1–36. Available: http://dx.doi.org/10.1007/978-3-319-27335-8_9-1.
SponsorsThis work was supported by ONR Grant #N00014-17-1-2060 and NSF Grant #ECCS-1638214 and by funding from King Abdullah University of Science and Technology (KAUST).
JournalHandbook of Dynamic Game Theory