In this paper, we consider a multi-objective optimization problem over networks in which agents aim to maximize their own objective function, while satisfying both local and coupling constraints. This set up includes, e.g., the computation of optimal steady states in multi-agent control systems. Since fairness is a key feature required for the solution, we resort to Cooperative Game Theory and search for the Nash bargaining solution among all the efficient (or Pareto optimal) points of a bargaining game. We propose a negotiation mechanism among the agents to compute such a solution in a distributed way. The problem is reformulated as the maximization of a properly weighted sum of the objective functions. The proposed algorithm is then a two step procedure in which local estimates of the Nash bargaining weights are updated online and existing distributed optimization algorithms are applied. The proposed method is formally analyzed for a particular case, while numerical simulations are provided to corroborate the theoretical findings and to demonstrate its efficacy.

A distributed optimization algorithm for Nash bargaining in multi-agent systems / Camisa A.; Köhler P.N.; Müller M.A.; Notarstefano G.; Allgöwer F.. - ELETTRONICO. - 53:2(2020), pp. 2684-2689. (Intervento presentato al convegno 21st IFAC World Congress 2020 tenutosi a Berlin, Germany nel 12-17 July 2020) [10.1016/j.ifacol.2020.12.402].

A distributed optimization algorithm for Nash bargaining in multi-agent systems

Camisa A.
;
Notarstefano G.;
2020

Abstract

In this paper, we consider a multi-objective optimization problem over networks in which agents aim to maximize their own objective function, while satisfying both local and coupling constraints. This set up includes, e.g., the computation of optimal steady states in multi-agent control systems. Since fairness is a key feature required for the solution, we resort to Cooperative Game Theory and search for the Nash bargaining solution among all the efficient (or Pareto optimal) points of a bargaining game. We propose a negotiation mechanism among the agents to compute such a solution in a distributed way. The problem is reformulated as the maximization of a properly weighted sum of the objective functions. The proposed algorithm is then a two step procedure in which local estimates of the Nash bargaining weights are updated online and existing distributed optimization algorithms are applied. The proposed method is formally analyzed for a particular case, while numerical simulations are provided to corroborate the theoretical findings and to demonstrate its efficacy.
2020
21th IFAC World Congress Berlin, Germany, 12-17 July 2020 PROCEEDINGS
2684
2689
A distributed optimization algorithm for Nash bargaining in multi-agent systems / Camisa A.; Köhler P.N.; Müller M.A.; Notarstefano G.; Allgöwer F.. - ELETTRONICO. - 53:2(2020), pp. 2684-2689. (Intervento presentato al convegno 21st IFAC World Congress 2020 tenutosi a Berlin, Germany nel 12-17 July 2020) [10.1016/j.ifacol.2020.12.402].
Camisa A.; Köhler P.N.; Müller M.A.; Notarstefano G.; Allgöwer F.
File in questo prodotto:
File Dimensione Formato  
nash_bargaining-ifac.pdf

accesso aperto

Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione - Non commerciale - Non opere derivate (CCBYNCND)
Dimensione 481.17 kB
Formato Adobe PDF
481.17 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/822585
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 1
social impact