### Abstract

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 language | English (US) |
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Title of host publication | International Mediterranean Modelling Multiconference, I3M |

Pages | 231-237 |

Number of pages | 7 |

State | Published - 2006 |

Event | International Mediterranean Modelling Multiconference, I3M 2006 - Barcelona, Spain Duration: Oct 4 2006 → Oct 6 2006 |

### Other

Other | International Mediterranean Modelling Multiconference, I3M 2006 |
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Country | Spain |

City | Barcelona |

Period | 10/4/06 → 10/6/06 |

### Fingerprint

### Keywords

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

### ASJC Scopus subject areas

- Modeling and Simulation

### Cite this

*International Mediterranean Modelling Multiconference, I3M*(pp. 231-237)

**An asymmetric multi-agent learning model and its simulation analysis.** / Qiao, Haiyan; Rozenblit, Jerzy W; Szidarovszky, Ferenc.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*International Mediterranean Modelling Multiconference, I3M.*pp. 231-237, International Mediterranean Modelling Multiconference, I3M 2006, Barcelona, Spain, 10/4/06.

}

TY - GEN

T1 - An asymmetric multi-agent learning model and its simulation analysis

AU - Qiao, Haiyan

AU - Rozenblit, Jerzy W

AU - Szidarovszky, Ferenc

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

KW - Asymmetric Nash bargaining solution

KW - Multi- agent learning

KW - Pareto-optimality

KW - Simulation

KW - Social dilemma

UR - http://www.scopus.com/inward/record.url?scp=84870208555&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84870208555&partnerID=8YFLogxK

M3 - Conference contribution

SN - 8469007262

SN - 9788469007266

SP - 231

EP - 237

BT - International Mediterranean Modelling Multiconference, I3M

ER -