Multi-stage discrete time dynamic average consensus

2016 ◽  
Author(s):  
Mauro Franceschelli ◽  
Andrea Gasparri
Keyword(s):  
Discrete Time ◽  
Time Dynamic ◽  
Average Consensus ◽  
Multi Stage

Multi-stage discrete time and randomized dynamic average consensus

Automatica ◽  
2019 ◽  
Vol 99 ◽  
pp. 69-81 ◽  
Author(s):  
Mauro Franceschelli ◽  
Andrea Gasparri
Keyword(s):  
Discrete Time ◽  
Average Consensus ◽  
Multi Stage

Step size analysis in discrete-time dynamic average consensus

2014 ◽  
Author(s):  
Eduardo Montijano ◽  
Juan I. Montijano ◽  
Carlos Sagues ◽  
Sonia Martinez
Keyword(s):  
Discrete Time ◽  
Size Analysis ◽  
Step Size ◽  
Time Dynamic ◽  
Average Consensus

Discrete-time dynamic average consensus

Automatica ◽  
2010 ◽  
Vol 46 (2) ◽  
pp. 322-329 ◽  
Author(s):  
Minghui Zhu ◽  
Sonia Martínez
Keyword(s):  
Discrete Time ◽  
Time Dynamic ◽  
Average Consensus

Robust discrete time dynamic average consensus

Automatica ◽  
2014 ◽  
Vol 50 (12) ◽  
pp. 3131-3138 ◽  
Author(s):  
Eduardo Montijano ◽  
Juan Ignacio Montijano ◽  
Carlos Sagüés ◽  
Sonia Martínez
Keyword(s):  
Discrete Time ◽  
Time Dynamic ◽  
Average Consensus

scholarly journals Search for a moving target in a competitive environment

2021 ◽  
Author(s):  
Benoit Duvocelle ◽  
János Flesch ◽  
Hui Min Shi ◽  
Dries Vermeulen
Keyword(s):  
Markov Chain ◽  
Discrete Time ◽  
Competitive Environment ◽  
Moving Target ◽  
Time Varying ◽  
Greedy Strategy ◽  
Time Dynamic ◽  
Dynamic Search ◽  
Subgame Perfect Equilibria ◽  
Perfect Equilibria

AbstractWe consider a discrete-time dynamic search game in which a number of players compete to find an invisible object that is moving according to a time-varying Markov chain. We examine the subgame perfect equilibria of these games. The main result of the paper is that the set of subgame perfect equilibria is exactly the set of greedy strategy profiles, i.e. those strategy profiles in which the players always choose an action that maximizes their probability of immediately finding the object. We discuss various variations and extensions of the model.

Sign in / Sign up

Export Citation Format

Share Document