An asymmetric multi-agent learning model and its simulation analysis

Haiyan Qiao, Jerzy Rozenblit, Ferenc Szidarovszky

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Multi-agent decision problems in unknown en- vironments are common where the agents are usually empowered with di®erent decision pow- ers and involved in some sort of the prisoner's dilemma problem. A general solution to this kind of complex decision problem is that the agents cooperate to play a joint action. Asym- metric Nash bargaining solution is an attractive approach to such cooperative games with players of di®erent powers. In this paper, a new multi- agent learning algorithm based on the asymmet- ric Nash bargaining solution is presented. Sim- ulation is performed on a testbed of stochastic games. The experimental results demonstrate that the algorithm is fast and converges to a Pareto-optimal solution. Compared with the learning algorithms based on non-cooperative equilibrium, this approach is faster and avoids the disturbing problem of equilibrium selection.

Original languageEnglish (US)
Title of host publicationInternational Mediterranean Modelling Multiconference, IMM
Number of pages7
StatePublished - 2006
EventInternational Mediterranean Modelling Multiconference, I3M 2006 - Barcelona, Spain
Duration: Oct 4 2006Oct 6 2006

Publication series

NameInternational Mediterranean Modelling Multiconference, I3M


OtherInternational Mediterranean Modelling Multiconference, I3M 2006


  • Asymmetric Nash bargaining solution
  • Multi- agent learning
  • Pareto-optimality
  • Simulation
  • Social dilemma

ASJC Scopus subject areas

  • Modeling and Simulation


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